NIPS 2015 and Machine Learning Research at Google

Posted by Sanjiv Kumar, Research Scientist

This week, Montreal hosts the 29^th Annual Conference on Neural Information Processing Systems (NIPS 2015), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of some of the latest in machine learning research. Google will have a strong presence at NIPS 2015, with over 140 Googlers attending in order to contribute to and learn from the broader academic research community by presenting technical talks and posters, in addition to hosting workshops and tutorials.

Research at Google is at the forefront of innovation in Machine Intelligence, actively exploring virtually all aspects of machine learning including classical algorithms as well as cutting-edge techniques such as deep learning. Focusing on both theory as well as application, much of our work on language understanding, speech, translation, visual processing, ranking, and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, and develop learning approaches to understand and generalize.

If you are attending NIPS 2015, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at NIPS 2015 in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of NIPS 2015.

PROGRAM ORGANIZERS
General Chairs
Corinna Cortes, Neil D. Lawrence
Program Committee includes:
Samy Bengio, Gal Chechik, Ian Goodfellow, Shakir Mohamed, Ilya Sutskever

ORAL SESSIONS
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Vitaly Kuznetsov, Mehryar Mohri

SPOTLIGHT SESSIONS
Distributed Submodular Cover: Succinctly Summarizing Massive Data
Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause

Spatial Transformer Networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu

Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly

Structured Transforms for Small-Footprint Deep Learning
Vikas Sindhwani, Tara Sainath, Sanjiv Kumar

Spherical Random Features for Polynomial Kernels
Jeffrey Pennington, Felix Yu, Sanjiv Kumar

POSTERS
Learning to Transduce with Unbounded Memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom

Deep Knowledge Tracing
Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein

Hidden Technical Debt in Machine Learning Systems
D Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, Dan Dennison

Grammar as a Foreign Language
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton

Stochastic Variational Information Maximisation
Shakir Mohamed, Danilo Rezende

Embedding Inference for Structured Multilabel Prediction
Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Bing Xu, Nan Ding, Dale Schuurmans

On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen, Nan Ding, Lawrence Carin

Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
Rakesh Shivanna, Bibaswan Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach

Differentially Private Learning of Structured Discrete Distributions
Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt

Nearly Optimal Private LASSO
Kunal Talwar, Li Zhang, Abhradeep Thakurta

Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa

Gradient Estimation Using Stochastic Computation Graphs
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer

Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom

Bayesian dark knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling

Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth

Semi-supervised Sequence Learning
Andrew Dai, Quoc Le

Natural Neural Networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, Koray Kavukcuoglu

Revenue Optimization against Strategic Buyers
Andres Munoz Medina, Mehryar Mohri


WORKSHOPS
Feature Extraction: Modern Questions and Challenges
Workshop Chairs include: Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar
Program Committee includes: Jeffery Pennington, Vikas Sindhwani

NIPS Time Series Workshop
Invited Speakers include: Mehryar Mohri
Panelists include: Corinna Cortes

Nonparametric Methods for Large Scale Representation Learning
Invited Speakers include: Amr Ahmed

Machine Learning for Spoken Language Understanding and Interaction
Invited Speakers include: Larry Heck

Adaptive Data Analysis
Organizers include: Moritz Hardt

Deep Reinforcement Learning
Organizers include : David Silver
Invited Speakers include: Sergey Levine

Advances in Approximate Bayesian Inference
Organizers include : Shakir Mohamed
Panelists include: Danilo Rezende

Cognitive Computation: Integrating Neural and Symbolic Approaches
Invited Speakers include: Ramanathan V. Guha, Geoffrey Hinton, Greg Wayne

Transfer and Multi-Task Learning: Trends and New Perspectives
Invited Speakers include: Mehryar Mohri
Poster presentations include: Andres Munoz Medina

Learning and privacy with incomplete data and weak supervision
Organizers include : Felix Yu
Program Committee includes: Alexander Blocker, Krzysztof Choromanski, Sanjiv Kumar
Speakers include: Nando de Freitas

Black Box Learning and Inference
Organizers include : Ali Eslami
Keynotes include: Geoff Hinton

Quantum Machine Learning
Invited Speakers include: Hartmut Neven

Bayesian Nonparametrics: The Next Generation
Invited Speakers include: Amr Ahmed

Bayesian Optimization: Scalability and Flexibility
Organizers include: Nando de Freitas

Reasoning, Attention, Memory (RAM)
Invited speakers include: Alex Graves, Ilya Sutskever

Extreme Classification 2015: Multi-class and Multi-label Learning in Extremely Large Label Spaces
Panelists include: Mehryar Mohri, Samy Bengio
Invited speakers include: Samy Bengio

Machine Learning Systems
Invited speakers include: Jeff Dean


SYMPOSIA
Brains, Mind and Machines
Invited Speakers include: Geoffrey Hinton, Demis Hassabis

Deep Learning Symposium
Program Committee Members include: Samy Bengio, Phil Blunsom, Nando De Freitas, Ilya Sutskever, Andrew Zisserman
Invited Speakers include: Max Jaderberg, Sergey Ioffe, Alexander Graves

Algorithms Among Us: The Societal Impacts of Machine Learning
Panelists include: Shane Legg


TUTORIALS
NIPS 2015 Deep Learning Tutorial
Geoffrey E. Hinton, Yoshua Bengio, Yann LeCun

Large-Scale Distributed Systems for Training Neural Networks
Jeff Dean, Oriol Vinyals

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Google Science Fair 2015 what will you try

Posted by Miriam Schneider, Google for Education team

(Cross-posted from the Google for Education Blog)

Science is about observing and experimenting. It’s about exploring unanswered questions, solving problems through curiosity, learning as you go and always trying again.

That’s the spirit behind the fifth annual Google Science Fair, kicking off today. Together with LEGO Education, National Geographic, Scientific American and Virgin Galactic, we’re calling on all young researchers, explorers, builders, technologists and inventors to try something ambitious. Something imaginative, or maybe even unimaginable. Something that might just change the world around us.

From now through May 18, students around the world ages 13-18 can submit projects online across all scientific fields, from biology to computer science to anthropology and everything in between. Prizes include $100,000 in scholarships and classroom grants from Scientific American and Google, a National Geographic Expedition to the Galapagos, an opportunity to visit LEGO designers at their Denmark headquarters, and the chance to tour Virgin Galactic’s new spaceship at their Mojave Air and Spaceport. This year we’re also introducing an award to recognize an Inspiring Educator, as well as a Community Impact Award honoring a project that addresses an environmental or health challenge.

It’s only through trying something that we can get somewhere. Flashlights required batteries, then Ann Makosinski tried the heat of her hand. His grandfather would wander out of bed at night, until Kenneth Shinozuka tried a wearable sensor. The power supply was constantly unstable in her Indian village, so Harine Ravichandran tried to build a different kind of regulator. Previous Science Fair winners have blown us away with their ideas. Now it’s your turn.

Big ideas that have the potential to make a big impact often start from something small. Something that makes you curious. Something you love, you’re good at, and want to try.

So, what will you try?

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Announcing Google’s 2015 Global PhD Fellows

Posted by Michael Rennaker, Google University Relations

In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its seventh year, our fellowship programs have collectively supported over 200 graduate students in Australia, China and East Asia, India, North America, Europe and the Middle East who seek to shape and influence the future of technology.

Reflecting our continuing commitment to building mutually beneficial relationships with the academic community, we are excited to announce the 44 students from around the globe who are recipients of the award. We offer our sincere congratulations to Google’s 2015 Class of PhD Fellows!

Australia

• Bahar Salehi, Natural Language Processing (University of Melbourne)
• Siqi Liu, Computational Neuroscience (University of Sydney)
• Qian Ge, Systems (University of New South Wales)

China and East Asia

• Bo Xin, Artificial Intelligence (Peking University)
• Xingyu Zeng, Computer Vision (The Chinese University of Hong Kong)
• Suining He, Mobile Computing (The Hong Kong University of Science and Technology)
• Zhenzhe Zheng, Mobile Networking (Shanghai Jiao Tong University)
• Jinpeng Wang, Natural Language Processing (Peking University)
• Zijia Lin, Search and Information Retrieval (Tsinghua University)
• Shinae Woo, Networking and Distributed Systems (Korea Advanced Institute of Science and Technology)
• Jungdam Won, Robotics (Seoul National University)

India

• Palash Dey, Algorithms (Indian Institute of Science)
• Avisek Lahiri, Machine Perception (Indian Institute of Technology Kharagpur)
• Malavika Samak, Programming Languages and Software Engineering (Indian Institute of Science)

Europe and the Middle East

• Heike Adel, Natural Language Processing (University of Munich)
• Thang Bui, Speech Technology (University of Cambridge)
• Victoria Caparrós Cabezas, Distributed Systems (ETH Zurich)
• Nadav Cohen, Machine Learning (The Hebrew University of Jerusalem)
• Josip Djolonga, Probabilistic Inference (ETH Zurich)
• Jakob Julian Engel, Computer Vision (Technische Universität München)
• Nikola Gvozdiev, Computer Networking (University College London)
• Felix Hill, Language Understanding (University of Cambridge)
• Durk Kingma, Deep Learning (University of Amsterdam)
• Massimo Nicosia, Statistical Natural Language Processing (University of Trento)
• George Prekas, Operating Systems (École Polytechnique Fédérale de Lausanne)
• Roman Prutkin, Graph Algorithms (Karlsruhe Institute of Technology)
• Siva Reddy, Multilingual Semantic Parsing (The University of Edinburgh)
• Immanuel Trummer, Structured Data Analysis (École Polytechnique Fédérale de Lausanne)
• Margarita Vald, Security (Tel Aviv University)

North America

• Waleed Ammar, Natural Language Processing (Carnegie Mellon University)
• Justin Meza, Systems Reliability (Carnegie Mellon University)
• Nick Arnosti, Market Algorithms (Stanford University)
• Osbert Bastani, Programming Languages (Stanford University)
• Saurabh Gupta, Computer Vision (University of California, Berkeley)
• Masoud Moshref Javadi, Computer Networking (University of Southern California)
• Muhammad Naveed, Security (University of Illinois at Urbana-Champaign)
• Aaron Parks, Mobile Networking (University of Washington)
• Kyle Rector, Human Computer Interaction (University of Washington)
• Riley Spahn, Privacy (Columbia University)
• Yun Teng, Computer Graphics (University of California, Santa Barbara)
• Carl Vondrick, Machine Perception, (Massachusetts Institute of Technology)
• Xiaolan Wang, Structured Data (University of Massachusetts Amherst)
• Tan Zhang, Mobile Systems (University of Wisconsin-Madison)
• Wojciech Zaremba, Machine Learning (New York University)

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Sergey and Larry awarded the Seoul Test of Time Award from WWW 2015

Posted by Andrei Broder, Google Distinguished Scientist

Today, at the 24th International World Wide Web Conference (WWW) in Florence, Italy, our company founders, Sergey Brin and Larry Page, received the inaugural Seoul Test-of-Time Award for their 1998 paper “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, which introduced Google to the world at the 7th WWW conference in Brisbane, Australia. I had the pleasure and honor to accept the award on behalf of Larry and Sergey from Professor Chin-Wan Chung, who led the committee that created the award.

Except for the fact that I was myself in Brisbane, it is hard to believe that Google began just as a two-student research project at Stanford University 17 years ago with the goal to “produce much more satisfying search results than existing systems.” Their paper presented two breakthrough concepts: first, using a distributed system built on inexpensive commodity hardware to deal with the size of the index, and second, using the hyperlink structure of the Web as a powerful new relevance signal. By now these ideas are common wisdom, but their paper continues to be very influential: it has over 13,000 citations so far and more are added every day.

Since those beginnings Google has continued to grow, with tools that enable small business owners to reach customers, help long lost friends to reunite, and empower users to discover answers. We keep pursuing new ideas and products, generating discoveries that both affect the world and advance the state-of-the-art in Computer Science and related disciplines. From products like Gmail, Google Maps and Google Earth Engine to advances in Machine Intelligence, Computer Vision, and Natural Language Understanding, it is our continuing goal to create useful tools and services that benefit our users.

Larry and Sergey sent a video message to the conference expressing their thanks and their encouragement for future research, in which Sergey said “There is still a ton of work left to do in Search, and on the Web as a whole and I couldn’t think of a more exciting time to be working in this space.” I certainly share this view, and was very gratified by the number of young computer scientists from all over the world that came by the Google booth at the conference to share their thoughts about the future of search, and to explore the possibility of joining our efforts.

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VLDB 2015 and Database Research at Google

Posted by Corinna Cortes, Head of Google Research NY and Cong Yu, Research Scientist

This week, Kohala, Hawaii hosts the 41st International Conference of Very Large Databases (VLDB 2015), a premier annual international forum for data management and database researchers, vendors, practitioners, application developers and users. As a leader in Database research, Google will have a strong presence at VLDB 2015 with many Googlers publishing work, organizing workshops and presenting demos.

The research Google is presenting at VLDB involves the work of Structured Data teams who are building intelligent and efficient systems to discover, annotate and explore structured data from the Web, surfacing them creatively through Google products (such as structured snippets and table search), as well as engineering efforts that create scalable, reliable, fast and general-purpose infrastructure for large-scale data processing (such as F1, Mesa, and Google Clouds BigQuery).

If you are attending VLDB 2015, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at VLDB 2015 in the list below (Googlers highlighted in blue).

Google is a Gold Sponsor of VLDB 2015.

Papers:
Keys for Graphs
Wenfei Fan, Zhe Fan, Chao Tian, Xin Luna Dong

In-Memory Performance for Big Data
Goetz Graefe, Haris Volos, Hideaki Kimura, Harumi Kuno, Joseph Tucek, Mark Lillibridge, Alistair Veitch

The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael Fernández-Moctezuma, Reuven Lax, Sam McVeety, Daniel Mills, Frances Perry, Eric Schmidt, Sam Whittle

Resource Bricolage for Parallel Database Systems
Jiexing Li, Jeffrey Naughton, Rimma Nehme

AsterixDB: A Scalable, Open Source BDMS
Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alex Behm, Vinayak Borkar, Yingyi Bu, Michael Carey, Inci Cetindil, Madhusudan Cheelangi, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann

Knowledge-Based Trust: A Method to Estimate the Trustworthiness of Web Sources
Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang, Wilko Horn, Camillo Lugaresi, Shaohua Sun, Wei Zhang

Efficient Evaluation of Object-Centric Exploration Queries for Visualization
You Wu, Boulos Harb, Jun Yang, Cong Yu

Interpretable and Informative Explanations of Outcomes
Kareem El Gebaly, Parag Agrawal, Lukasz Golab, Flip Korn, Divesh Srivastava

Take me to your leader! Online Optimization of Distributed Storage Configurations
Artyom Sharov, Alexander Shraer, Arif Merchant, Murray Stokely

TreeScope: Finding Structural Anomalies In Semi-Structured Data
Shanshan Ying, Flip Korn, Barna Saha, Divesh Srivastava

Workshops:
Workshop on Big-Graphs Online Querying - Big-O(Q) 2015
Workshop co-chair: Cong Yu

3rd International Workshop on In-Memory Data Management and Analytics
Program committee includes: Sandeep Tata

High-Availability at Massive Scale: Building Googles Data Infrastructure for Ads
Invited talk at BIRTE by: Ashish Gupta, Jeff Shute

Demonstrations:
KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing
Xu Chu, John Morcos, Ihab Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, Yin Ye

Error Diagnosis and Data Profiling with Data X-Ray
Xiaolan Wang, Mary Feng, Yue Wang, Xin Luna Dong, Alexandra Meliou

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Announcing the 2015 North American Google PhD Fellows

Posted by Michael Rennaker, Google University Relations

In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional work in Computer Science (CS) and related disciplines. In that time we’ve seen past recipients add depth and breadth to CS by developing new ideas and research directions, from building new intelligence models to changing the way in which we interact with computers to advancing into faculty positions, where they go on to train the next generation of researchers.

Reflecting our continuing commitment to building strong relations with the global academic community, we are excited to announce the latest North American Google PhD Fellows. The following 15 fellowship recipients were chosen from a highly competitive group, and represent the outstanding quality of nominees provided by our university partners:

• Justin Meza, Google US/Canada Fellowship in Systems Reliability (Carnegie Mellon University)
• Waleed Ammar, Google US/Canada Fellowship in Natural Language Processing (Carnegie Mellon University)
• Aaron Parks, Google US/Canada Fellowship in Mobile Networking (University of Washington)
• Kyle Rector, Google US/Canada Fellowship in Human Computer Interaction (University of Washington)
• Nick Arnosti, Google US/Canada Fellowship in Market Algorithms (Stanford University)
• Osbert Bastani, Google US/Canada Fellowship in Programming Languages (Stanford University)
• Carl Vondrick, Google US/Canada Fellowship in Machine Perception, (Massachusetts Institute of Technology)
• Wojciech Zaremba, Google US/Canada Fellowship in Machine Learning (New York University)
• Xiaolan Wang, Google US/Canada Fellowship in Structured Data (University of Massachusetts Amherst)
• Muhammad Naveed, Google US/Canada Fellowship in Security (University of Illinois at Urbana-Champaign)
• Masoud Moshref Javadi, Google US/Canada Fellowship in Computer Networking (University of Southern California)
• Riley Spahn, Google US/CanadaFellowship in Privacy (Columbia University)
• Saurabh Gupta, Google US/Canada Fellowship in Computer Vision (University of California, Berkeley)
• Yun Teng, Google US/Canada Fellowship in Computer Graphics (University of California, Santa Barbara)
• Tan Zhang, Google US/Canada Fellowship in Mobile Systems (University of Wisconsin-Madison)

This group of students represent the next generation of researchers who endeavor to solve some of the most interesting challenges in Computer Science. We offer our congratulations, and look forward to their future contributions to the research community with high expectations.

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Dr Gene Amdahl November 16 1922 – November 10 2015

Computer Pioneer, Mainframe Computer Architect & Entrepreneur

He is remembered for Amdahls law that basically states that the intrinsically serial part of any algorithm will limit the benefits from parallel processing. He was one of the architects of the IBM System/360 line of computers that dominated the mainframe industry and are still a force today. He was a champion of high-end uniprocessing.
Read his biography in the New York Times.
   I am grateful to Dr Amdahl for creating a company at which I was able to work as a computer architect for 6 years, 1976-82 and consult at for more. I did not have much to do with him directly – he was way above my level and he left to form a new company in 1979. But some anecdotes are in order.
   Dr Amdahl was revered by his Engineering Staff. He had a great knack of inventing rules of thumb to guide the designers. One was "one megabyte of I/O for every MIP of performance". Another, I remember vaguely, was that "caches should be square" (as many lines as bits in a line.) These were not necessarily correct rules but it was better to make the decision and get down to design rather than dithering (even better, of course, to measure and model.) He also had sound advice on functional architecture for speed – when you see an instructions operation code, you should immediately know the operand address and where the next instruction is located.
   I noticed that Dr Amdahl was allowed to have opinions but he insisted that those with contrary opinions had to have facts. As the technology and markets changed, many of his assumptions had to be challenged. I was with the group that developed the high level characteristics of the 580. I remember us having to justify 2-way multiprocessing as being required because our main market was for capacity rather than solely for speed and one could no longer design uniprocessors that could compete. I remember once having to tell him that his favorite division algorithm as used in the 470V (involving finding the reciprocal using Newtons formula and multiplication) was actually slower than the expected speed of straightforward non-restoring division. When our arguments were solid he just accepted them and moved-on.  He had to also accept microprogramming and memory chips for registers but I recall he embraced this and came up himself with a clever microcode scheme with two microstores, accessed simultaneously and each microinstruction including an address for the other memory – so each microinstruction could branch with no penalty.
   My closest encounter with Dr Amdahl was when he was trying to get the 580 design started. The 470V used 100-gate ECL chips with 80 I/O pins. Fujitsu was developing the next technology with 400 gates per chip but still with 80 I/O. The Amdahl Corp. engineers were adamant that they couldnt design a faster computer and reduce the chip count because the I/O count was not adequate (this was because even with 100 gates they could not use them all in some cases and the famous "Rents Rule" requiring I/O to rise with chip density.)  Dr Amdahl was dis-satisfied and hatched a plan to jolt the engineers into creativity by engaging with the Architecture group over the design.
   So, Inder Singh (later to found the Ethernet switching company Excelan) and I found ourselves closeted with Dr Amdahl in his office. He presented us with copies of his handwritten logic for the 580 execution unit (a piece is below and I have put the whole thing on the web as it must be the last time when a major computers logic was specified in handwriting.) Our meeting was on a Friday and he left us with the task of coming up with a design by the following Monday!

   My wife and I were going to Carmel for that weekend but I decided that would make no difference to my chances of arriving at a design by Monday so we went anyway. The Monday meeting was delayed until Thursday (phew!) and by then Inder and I had come up with some evidence that the execution unit could be "bit sliced" with multiple levels of logic within the chip (this was not at all original as there were microprocessors taking the same approach.) Basically we said "yes sir, you are right, it can be done. He then took a team of engineers with him on a sales trip to Scandinavia where they had to stay in the hotels and work on the new design – as a result the 580 was code-named Oslo. I think that part of the problem was that Engineers preferred circuit diagrams to logic equations but Dr Amdahl was a logic whizz.
It is hard to gauge the impact of Dr Amdahl and his engineering team on the process of designing fast computers but, if it could be known, I am sure that it would be profound. The Amdahl approach of making the processor pipeline central to the design, demonstrating that pipeline interlocks could be controlled without loss of performance (itself derived form earlier IBM projects), and that fast computers could be microprogrammed,  gradually became known throughout the "valley" as design engineers moved on to new jobs.

Amdahl, the man and his company, were quite a phenomenon!

[Post written by Bob Doran]

from The Universal Machine http://universal-machine.blogspot.com/

Put the internet to work for you.

Turn off or edit this Recipe

Recommended for you

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The iSANZ 2015 Best International Superstar award goes to

Our very own Clarke Thomborson! The iSANZ Awards are a showcase of excellence in New Zealand information security. Their mission is to formally recognise the achievements of outstanding New Zealand InfoSec professionals, companies and initiatives / events. The Best International Superstar award category is open to individuals who achieved significant results in the development or promotion of work that has had a high international profile. Clarke won the award for his contributions in trust, identity and privacy management which have helped significantly raise the profile of ICT within New Zealand.

from The Universal Machine http://universal-machine.blogspot.com/

Put the internet to work for you.

Turn off or edit this Recipe

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Google Faculty Research Awards Summer 2015

posted by Maggie Johnson, Director of Education and University Relations

We have just completed another round of the Google Faculty Research Awards, our annual open call for research proposals on Computer Science and related topics, including systems, machine learning, software engineering, security and mobile. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 805 proposals, about the same as last round, covering 48 countries on 6 continents. After expert reviews and committee discussions, we decided to fund 113 projects, with 27% of the funding awarded to universities outside the U.S. The subject areas that received the highest level of support were systems, machine perception, software engineering, and machine learning.

The Faculty Research Awards program plays a critical role in building and maintaining strong collaborations with top research faculty globally. These relationships allow us to keep a pulse on what’s happening in academia in strategic areas, and they help to extend our research capabilities and programs. Faculty also report, through our annual survey, that they and their students benefit from a direct connection to Google as a source of ideas and perspective.

Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is October 15), please visit our website for more information.

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Google Faculty Research Awards Winter 2015

Posted by Maggie Johnson, Director of Education and University Relations

We have just completed another round of the Google Faculty Research Awards, our biannual open call for research proposals on Computer Science and related topics, including systems, machine perception, structured data, robotics, and mobile. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 808 proposals, an increase of 12% over last round, covering 55 countries on 6 continents. After expert reviews and committee discussions, we decided to fund 122 projects, with 20% of the funding awarded to universities outside the U.S. The subject areas that received the highest level of support were systems, human-computer interaction, and machine perception.

The Faculty Research Award program enables us to build strong relationships with faculty around the world who are pursuing innovative research, and plays an important role for Google’s Research organization by fostering an exchange of ideas that advances the state of the art. Each round, we receive proposals from faculty who may be just starting their careers, or who might be experimenting in new areas that help us look forward and innovate on whats emerging in the CS community.

Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is April 15), please visit our website for more information.

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Announcing the 2015 Google European Doctoral Fellows

Posted by David Harper, University Relations and Beate List, Research Programs

In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional work in Computer Science and related disciplines. The following year, we launched the program in Europe as the Google European Doctoral Fellowship program. Alumni of the European program occupy a variety of positions including faculty positions (Ofer Meshi, Cynthia Liem), academic research positions (Roland Angst, Carola Doerr née Winzen) and positions in industry (Yair Adato, Peter Hosek, Neil Houlsby).

Reflecting our continuing commitment to building strong relations with the European academic community, we are delighted to announce the 2015 Google European Doctoral Fellows. The following fifteen fellowship recipients were selected from an outstanding set of PhD students nominated by our partner universities:

• Heike Adel, Fellowship in Natural Language Processing (University of Munich)
• Thang Bui, Fellowship in Speech Technology (University of Cambridge)
• Victoria Caparrós Cabezas, Fellowship in Distributed Systems (ETH Zurich)
• Nadav Cohen, Fellowship in Machine Learning (The Hebrew University of Jerusalem)
• Josip Djolonga, Fellowship in Probabilistic Inference (ETH Zurich)
• Jakob Julian Engel, Fellowship in Computer Vision (Technische Universität München)
• Nikola Gvozdiev, Fellowship in Computer Networking (University College London)
• Felix Hill, Fellowship in Language Understanding (University of Cambridge)
• Durk Kingma, Fellowship in Deep Learning (University of Amsterdam)
• Massimo Nicosia, Fellowship in Statistical Natural Language Processing (University of Trento)
• George Prekas, Fellowship in Operating Systems (École Polytechnique Fédérale de Lausanne)
• Roman Prutkin, Fellowship in Graph Algorithms (Karlsruhe Institute of Technology)
• Siva Reddy, Fellowship in Multilingual Semantic Parsing (The University of Edinburgh)
• Immanuel Trummer, Fellowship in Structured Data Analysis (École Polytechnique Fédérale de Lausanne)
• Margarita Vald, Fellowship in Security (Tel Aviv University)

This group of students represent the next generation of researchers who will endeavor to solve some of the most interesting challenges in Computer Science. We offer our congratulations, and look forward to their future contributions to the research community with high expectation.

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ICML 2015 and Machine Learning Research at Google

Posted by Corinna Cortes, Head, Google Research NY

This week, Lille, France hosts the 2015 International Conference on Machine Learning (ICML 2015), a premier annual Machine Learning event supported by the International Machine Learning Society (IMLS). As a leader in Machine Learning research, Google will have a strong presence at ICML 2015, with many Googlers publishing work and hosting workshops. If you’re attending, we hope you’ll visit the Google booth and talk with the Googlers to learn more about the hard work, creativity and fun that goes into solving interesting ML problems that impacts millions of people. You can also learn more about our research being presented at ICML 2015 in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of ICML 2015.

ICML Program Committee
Area Chair - Corinna Cortes & Samy Bengio
IMLS Board Member - Corinna Cortes

Papers:
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas

BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Stephan Gouws, Yoshua Bengio, Greg Corrado

An Empirical Exploration of Recurrent Network Architectures
Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe, Christian Szegedy

DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra

Variational Inference with Normalizing Flows
Danilo Rezende, Shakir Mohamed

Structural Maxent Models
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

Weight Uncertainty in Neural Network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra

MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

Fictitious Self-Play in Extensive-Form Games
Johannes Heinrich, Marc Lanctot, David Silver

Universal Value Function Approximators
Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

Workshops:
Extreme Classification: Learning with a Very Large Number of Labels
Samy Bengio - Organizing Committee

Machine Learning for Education
Jonathan Huang - Organizing Committee

Workshop on Machine Learning Open Source Software 2015: Open Ecosystems
Ian Goodfellow - Program Committee

Machine Learning for Music Recommendation
Philippe Hamel - Invited Speaker

Large-Scale Kernel Learning: Challenges and New Opportunities
Poster - Just-In-Time Kernel Regression for Expectation Propagation
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S.M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltan Szabo

European Workshop on Reinforcement Learning (EWRL)
Rémi Munos - Organizing Committee
David Silver - Keynote

Workshop on Deep Learning
Geoff Hinton - Organizer
Tara Sainath, Oriol Vinyals, Ian Goodfellow, Karol Gregor - Invited Speakers
Poster - A Neural Conversational Model
Oriol Vinyals, Quoc Le
Oral Presentation - Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver

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2015 The Year Wearables Became More Than a Bad Word

Happy New Year. This blog is back from its annual holiday. Some of you will probably have received a fitness tracker, such as a Fitbit, for a Xmas present. So-called "wearables" had a very good year in 2015 and 2016 look to be even better. This article in Wired explores the trends and suggests that wearables are soon to become even more wearable.


from The Universal Machine http://universal-machine.blogspot.com/

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KDD 2015 Best Research Paper Award “Algorithms for Public Private Social Networks”

Posted by Posted by Corinna Cortes, Head, Google Research NY

The 21st ACM conference on Knowledge Discovery and Data Mining (KDD’15), a main venue for academic and industry research in data management, information retrieval, data mining and machine learning, was held last week in Sydney, Australia. In the past several years, Google has been actively participating in KDD, with several Googlers presenting work at the conference in the research and industrial tracks. This year Googlers presented 12 papers at KDD (listed below, with Googlers in blue), all of which are freely available at the ACM Digital Library.

One of these papers, Efficient Algorithms for Public-Private Social Networks, co-authored by Googlers Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni, former Googler intern Alessandro Epasto and research visitor Flavio Chierichetti, was awarded Best Research Paper. The inspiration for this paper comes from studying social networks and the importance of addressing privacy issues in analyzing such networks.

Privacy issues dictate the way information is shared among the members of the social network. In the simplest case, a user can mark some of her friends as private; this would make the connections (edges) between this user and these friends visible only to the user. In a different instantiation of privacy, a user can be a member of a private group; in this case, all the edges among the group members are to be considered private. Thus, each user in the social network has her own view of the link structure of the network. These privacy issues also influence the way in which the network itself can be viewed and processed by algorithms. For example, one cannot use the list of private friends of user X for suggesting potential friends or public news items to another user on the network, but one can use this list for the purpose of suggesting friends for user X.

As a result, enforcing these privacy guarantees translates to solving a different algorithmic problem for each user in the network, and for this reason, developing algorithms that process these social graphs and respect these privacy guarantees can become computationally expensive. In a recent study, Dey et al. crawled a snapshot of 1.4 million New York City Facebook users and reported that 52.6% of them hid their friends list. As more users make a larger portion of their social neighborhoods private, these computational issues become more important.

Motivated by the above, this paper introduces the public-private model of graphs, where each user (node) in the public graph has an associated private graph. In this model, the public graph is visible to everyone, and the private graph at each node is visible only to each specific user. Thus, any given user sees their graph as a union of their private graph and the public graph.

From algorithmic point of view, the paper explores two powerful computational paradigms for efficiently studying large graphs, namely, sketching and sampling, and focuses on some key problems in social networks such as similarity ranking, and clustering. In the sketching model, the paper shows how to efficiently approximate the neighborhood function, which in turn can be used to approximate various notions of centrality scores for each node - such centrality scores like the PageRank score have important applications in ranking and recommender systems. In the sampling model, the paper focuses on all-pair shortest path distances, node similarities, and correlation clustering, and develop algorithms that computes these notions on a given public-private graph and at the same time. The paper also illustrates the effectiveness of this model and the computational efficiency of the algorithms by performing experiments on real-world social networks.

The public-private model is an abstraction that can be used to develop efficient social network algorithms. This work leaves a number of open interesting research directions such as: obtaining efficient algorithms for the densest subgraph/community detection problems, influence maximization, computing other pairwise similarity scores, and most importantly, recommendation systems.

KDD’15 Papers, co-authored by Googlers:

Efficient Algorithms for Public-Private Social Networks (Best Paper Award)
Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab Mirrokni

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling

TimeMachine: Timeline Generation for Knowledge-Base Entities
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang

Algorithmic Cartography: Placing Points of Interest and Ads on Maps
Mohammad Mahdian, Okke Schrijvers, Sergei Vassilvitskii

Stream Sampling for Frequency Cap Statistics
Edith Cohen

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J.Smola, Le Song

Adaptation Algorithm and Theory Based on Generalized Discrepancy
Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina (now at Google)

Estimating Local Intrinsic Dimensionality
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle Ken-ichi Kawarabayashi, Michael Nett

Unified and Contrasting Cuts in Multiple Graphs: Application to Medical Imaging Segmentation
Chia-Tung Kuo, Xiang Wang, Peter Walker, Owen Carmichael, Jieping Ye, Ian Davidson

Going In-depth: Finding Longform on the Web
Virginia Smith, Miriam Connor, Isabelle Stanton

Annotating needles in the haystack without looking: Product information extraction from emails
Weinan Zhang, Amr Ahmed, Jie Yang, Vanja Josifovski, Alexander Smola

Focusing on the Long-term: Its Good for Users and Business
Diane Tang, Henning Hohnhold, Deirdre OBrien

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ICSE 2015 and Software Engineering Research at Google

Posted by Mohsen Vakilian, Software Engineer

The large scale of our software engineering efforts at Google often pushes us to develop cutting-edge infrastructure. In May 2015, at the International Conference on Software Engineering (ICSE 2015), we shared some of our software engineering tools and practices and collaborated with the research community through a combination of publications, committee memberships, and workshops. Learn more about some of our research below (Googlers highlighted in blue).

Google was a Gold supporter of ICSE 2015.

Technical Research Papers:
A Flexible and Non-intrusive Approach for Computing Complex Structural Coverage Metrics
Michael W. Whalen, Suzette Person, Neha Rungta, Matt Staats, Daniela Grijincu

Automated Decomposition of Build Targets
Mohsen Vakilian, Raluca Sauciuc, David Morgenthaler, Vahab Mirrokni

Tricorder: Building a Program Analysis Ecosystem
Caitlin Sadowski, Jeffrey van Gogh, Ciera Jaspan, Emma Soederberg, Collin Winter

Software Engineering in Practice (SEIP) Papers:
Comparing Software Architecture Recovery Techniques Using Accurate Dependencies
Thibaud Lutellier, Devin Chollak, Joshua Garcia, Lin Tan, Derek Rayside, Nenad Medvidovic, Robert Kroeger

Technical Briefings:
Software Engineering for Privacy in-the-Large
Pauline Anthonysamy, Awais Rashid

Workshop Organizers:
2nd International Workshop on Requirements Engineering and Testing (RET 2015)
Elizabeth Bjarnason, Mirko Morandini, Markus Borg, Michael Unterkalmsteiner, Michael Felderer, Matthew Staats

Committee Members:
Caitlin Sadowski - Program Committee Member and Distinguished Reviewer Award Winner
James Andrews - Review Committee Member
Ray Buse - Software Engineering in Practice (SEIP) Committee Member and Demonstrations Committee Member
John Penix - Software Engineering in Practice (SEIP) Committee Member
Marija Mikic - Poster Co-chair
Daniel Popescu and Ivo Krka - Poster Committee Members

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Google Computer Vision research at CVPR 2015

Posted by Vincent Vanhoucke, Google Research Scientist

Much of the worlds data is in the form of visual media. In order to utilize meaningful information from multimedia and deliver innovative products, such as Google Photos, Google builds machine-learning systems that are designed to enable computer perception of visual input, in addition to pursuing image and video analysis techniques focused on image/scene reconstruction and understanding.

This week, Boston hosts the 2015 Conference on Computer Vision and Pattern Recognition (CVPR 2015), the premier annual computer vision event comprising the main CVPR conference and several co-located workshops and short courses. As a leader in computer vision research, Google will have a strong presence at CVPR 2015, with many Googlers presenting publications in addition to hosting workshops and tutorials on topics covering image/video annotation and enhancement, 3D analysis and processing, development of semantic similarity measures for visual objects, synthesis of meaningful composites for visualization/browsing of large image/video collections and more.

Learn more about some of our research in the list below (Googlers highlighted in blue). If you are attending CVPR this year, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for hundreds of millions of people. Members of the Jump team will also have a prototype of the camera on display and will be showing videos produced using the Jump system on Google Cardboard.

Tutorials:
Applied Deep Learning for Computer Vision with Torch
Koray Kavukcuoglu, Ronan Collobert, Soumith Chintala

DIY Deep Learning: a Hands-On Tutorial with Caffe
Evan Shelhamer, Jeff Donahue, Yangqing Jia, Jonathan Long, Ross Girshick

ImageNet Large Scale Visual Recognition Challenge Tutorial
Olga Russakovsky, Jonathan Krause, Karen Simonyan, Yangqing Jia, Jia Deng, Alex Berg, Fei-Fei Li

Fast Image Processing With Halide
Jonathan Ragan-Kelley, Andrew Adams, Fredo Durand

Open Source Structure-from-Motion
Matt Leotta, Sameer Agarwal, Frank Dellaert, Pierre Moulon, Vincent Rabaud

Oral Sessions:
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection
George Papandreou, Iasonas Kokkinos, Pierre-André Savalle

Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time
Richard A. Newcombe, Dieter Fox, Steven M. Seitz

Show and Tell: A Neural Image Caption Generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell

Visual Vibrometry: Estimating Material Properties from Small Motion in Video
Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Frédo Durand, William T. Freeman

Fast Bilateral-Space Stereo for Synthetic Defocus
Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernández

Poster Sessions:
Learning Semantic Relationships for Better Action Retrieval in Images
Vignesh Ramanathan, Congcong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang Song, Samy Bengio, Charles Rosenberg, Li Fei-Fei

FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff, Dmitry Kalenichenko, James Philbin

A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, Andrew C. Gallagher

Best-Buddies Similarity for Robust Template Matching
Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman

Articulated Motion Discovery Using Pairs of Trajectories
Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

Reflection Removal Using Ghosting Cues
YiChang Shih, Dilip Krishnan, Frédo Durand, William T. Freeman

P3.5P: Pose Estimation with Unknown Focal Length
Changchang Wu

MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, Alexander C. Berg

Inferring 3D Layout of Building Facades from a Single Image
Jiyan Pan, Martial Hebert, Takeo Kanade

The Aperture Problem for Refractive Motion
Tianfan Xue, Hossein Mobahei, Frédo Durand, William T. Freeman

Video Magnification in Presence of Large Motions
Mohamed Elgharib, Mohamed Hefeeda, Frédo Durand, William T. Freeman

Robust Video Segment Proposals with Painless Occlusion Handling
Zhengyang Wu, Fuxin Li, Rahul Sukthankar, James M. Rehg

Ontological Supervision for Fine Grained Classification of Street View Storefronts
Yair Movshovitz-Attias, Qian Yu, Martin C. Stumpe, Vinay Shet, Sacha Arnoud, Liron Yatziv

VIP: Finding Important People in Images
Clint Solomon Mathialagan, Andrew C. Gallagher, Dhruv Batra

Fusing Subcategory Probabilities for Texture Classification
Yang Song, Weidong Cai, Qing Li, Fan Zhang

Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici

Workshops:
THUMOS Challenge 2015
Program organizers include: Alexander Gorban, Rahul Sukthankar

DeepVision: Deep Learning in Computer Vision 2015
Invited Speaker: Rahul Sukthankar

Large Scale Visual Commerce (LSVisCom)
Panelist: Luc Vincent

Large-Scale Video Search and Mining (LSVSM)
Invited Speaker and Panelist: Rahul Sukthankar
Program Committee includes: Apostol Natsev

Vision meets Cognition: Functionality, Physics, Intentionality and Causality
Program Organizers include: Peter Battaglia

Big Data Meets Computer Vision: 3rd International Workshop on Large Scale Visual Recognition and Retrieval (BigVision 2015)
Program Organizers include: Samy Bengio
Includes speaker Christian Szegedy - “Scalable approaches for large scale vision”

Observing and Understanding Hands in Action (Hands 2015)
Program Committee includes: Murphy Stein

Fine-Grained Visual Categorization (FGVC3)
Program Organizers include: Anelia Angelova

Large-scale Scene Understanding Challenge (LSUN)
Winners of the Scene Classification Challenge: Julian Ibarz, Christian Szegedy and Vincent Vanhoucke
Winners of the Caption Generation Challenge: Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan

Looking from above: when Earth observation meets vision (EARTHVISION)
Technical Committee includes: Andreas Wendel

Computer Vision in Vehicle Technology: Assisted Driving, Exploration Rovers, Aerial and Underwater Vehicles
Invited Speaker: Andreas Wendel
Program Committee includes: Andreas Wendel

Women in Computer Vision (WiCV)
Invited Speaker: Mei Han

ChaLearn Looking at People (sponsor)

Fine-Grained Visual Categorization (FGVC3) (sponsor)

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