Why attend USENIX Enigma?

Parisa Tabriz, Security Princess & Enigma Program Co-Chair

Last August, we announced USENIX Enigma, a new conference intended to shine a light on great, thought-provoking research in security, privacy, and electronic crime. With Enigma beginning in just a few short weeks, I wanted to share a couple of the reasons I’m personally excited about this new conference.

Enigma aims to bridge the divide that exists between experts working in academia, industry, and public service, explicitly bringing researchers from different sectors together to share their work. Our speakers include those spearheading the defense of digital rights (Electronic Frontier Foundation, Access Now), practitioners at a number of well known industry leaders (Akamai, Blackberry, Facebook, LinkedIn, Netflix, Twitter), and researchers from multiple universities in the U.S. and abroad. With the diverse session topics and organizations represented, I expect interesting—and perhaps spirited—coffee break and lunchtime discussions among the equally diverse list of conference attendees.

Of course, I’m very proud to have some of my Google colleagues speaking at Enigma:

• Adrienne Porter Felt will talk about blending research and engineering to solve usable security problems. You’ll hear how Chrome’s usable security team runs user studies and experiments to motivate engineering and design decisions. Adrienne will share the challenges they’ve faced when trying to adapt existing usable security research to practice, and give insight into how they’ve achieved successes.
• Ben Hawkes will be speaking about Project Zero, a security research team dedicated to the mission of, “making 0day hard.” Ben will talk about why Project Zero exists, and some of the recent trends and technologies that make vulnerability discovery and exploitation fundamentally harder.
• Kostya Serebryany will be presenting a 3-pronged approach to securing C++ code based on his many years of experiencing wrangling complex, buggy software. Kostya will survey multiple dynamic sanitizing tools him and his team have made publicly available, review control-flow and data-flow guided fuzzing, and explain a method to harden your code in the presence of any bugs that remain.
• Elie Bursztein will go through key lessons the Gmail team learned over the past 11 years while protecting users from spam, phishing, malware, and web attacks. Illustrated with concrete numbers and examples from one of the largest email systems on the planet, attendees will gain insight into specific techniques and approaches useful in fighting abuse and securing their online services.

In addition to raw content, my Program Co-Chair, David Brumley, and I have prioritized talk quality. Researchers dedicate months or years of their time to thinking about a problem and conducting the technical work of research, but a common criticism of technical conferences is that the actual presentation of that research seems like an afterthought. Rather than be a regurgitation of a research paper in slide format, a presentation is an opportunity for a researcher to explain the context and impact of their work in their own voice; a chance to inspire the audience to want to learn more or dig deeper. Taking inspiration from the TED conference, Enigma will have shorter presentations, and the program committee has worked with each speaker to help them craft the best version of their talk.

Hope to see some of you at USENIX Enigma later this month!

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

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 Cloud's 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 Google's 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

Pulling Back the Curtain on Google’s Network Infrastructure

Posted by Amin Vahdat, Google Fellow

Pursuing Google’s mission of organizing the world’s information to make it universally accessible and useful takes an enormous amount of computing and storage. In fact, it requires coordination across a warehouse-scale computer. Ten years ago, we realized that we could not purchase, at any price, a datacenter network that could meet the combination of our scale and speed requirements. So, we set out to build our own datacenter network hardware and software infrastructure. Today, at the ACM SIGCOMM conference, we are presenting a paper with the technical details on five generations of our in-house data center network architecture. This paper presents the technical details behind a talk we presented at Open Network Summit a few months ago.

From relatively humble beginnings, and after a misstep or two, we’ve built and deployed five generations of datacenter network infrastructure. Our latest-generation Jupiter network has improved capacity by more than 100x relative to our first generation network, delivering more than 1 petabit/sec of total bisection bandwidth. This means that each of 100,000 servers can communicate with one another in an arbitrary pattern at 10Gb/s.

Such network performance has been tremendously empowering for Google services. Engineers were liberated from optimizing their code for various levels of bandwidth hierarchy. For example, initially there were painful tradeoffs with careful data locality and placement of servers connected to the same top of rack switch versus correlated failures caused by a single switch failure. A high performance network supporting tens of thousands of servers with flat bandwidth also enabled individual applications to scale far beyond what was otherwise possible and enabled tight coupling among multiple federated services. Finally, we were able to substantially improve the efficiency of our compute and storage infrastructure. As quantified in this recent paper, scheduling a set of jobs over a single larger domain supports much higher utilization than scheduling the same jobs over multiple smaller domains.

Delivering such a network meant we had to solve some fundamental problems in networking. Ten years ago, networking was defined by the interaction of individual hardware elements, e.g., switches, speaking standardized protocols to dynamically learn what the network looks like. Based on this dynamically learned information, switches would set their forwarding behavior. While robust, these protocols targeted deployment environments with perhaps tens of switches communicating between between multiple organizations. Configuring and managing switches in such an environment was manual and error prone. Changes in network state would spread slowly through the network using a high-overhead broadcast protocol. Most challenging of all, the system could not scale to meet our needs.

We adopted a set of principles to organize our networks that is now the primary driver for networking research and industrial innovation, Software Defined Networking (SDN). We observed that we could arrange emerging merchant switch silicon around a Clos topology to scale to the bandwidth requirements of a data center building. The topology of all five generations of our data center networks follow the blueprint below. Unfortunately, this meant that we would potentially require 10,000+ individual switching elements. Even if we could overcome the scalability challenges of existing network protocols, managing and configuring such a vast number of switching elements would be impossible.

So, our next observation was that we knew the exact configuration of the network we were trying to build. Every switch would play a well-defined role in a larger-ensemble. That is, from the perspective of a datacenter building, we wished to build a logical building-scale network. For network management, we built a single configuration for the entire network and pushed the single global configuration to all switches. Each switch would simply pull out its role in the larger whole. Finally, we transformed routing from a pair-wise, fully distributed (but somewhat slow and high overhead) scheme to a logically-centralized scheme under the control of a single dynamically-elected master as illustrated in the figure below from our paper.

Our SIGCOMM paper on Jupiter is one of four Google-authored papers being presented at that conference. Taken together, these papers show the benefits of embedding research within production teams, reflecting both collaborations with PhD students carrying out extended research efforts with Google engineers during internships as well as key insights from deployed production systems:

• Our work on Bandwidth Enforcer shows how we can allocate wide area bandwidth among tens of thousands of individual applications based on centrally configured policy, substantially improving network utilization while simultaneously isolating services from one another.
• Condor addresses the challenges of designing data center network topologies. Network designers can specify constraints for data center networks; Condor efficiently generates candidate network designs that meet these constraints, and evaluates these candidates against a variety of target metrics.
• Congestion control in datacenter networks is challenging because of tiny buffers and very small round trip times. TIMELY shows how to manage datacenter bandwidth allocation while maintaining highly responsive and low latency network roundtrips in the data center.

These efforts reflect the latest in a long series of substantial Google contributions in networking. We are excited about being increasingly open about results of our research work: to solicit feedback on our approach, to influence future research and development directions so that we can benefit from community-wide efforts to improve networking, and to attract the next-generation of great networking thinkers and builders to Google. Our focus on Google Cloud Platform further increases the importance of being open about our infrastructure. Since the same network powering Google infrastructure for a decade is also the underpinnings of our Cloud Platform, all developers can leverage the network to build highly robust, manageable, and globally scalable services.

Say hello to the Enigma conference

Posted by Elie Bursztein - Anti-abuse team, Parisa Tabriz - Chrome Security and Niels Provos - Security team

USENIX Enigma is a new conference focused on security, privacy and electronic crime through the lens of emerging threats and novel attacks. The goal of this conference is to help industry, academic, and public-sector practitioners better understand the threat landscape. Enigma will have a single track of 30-minute talks that are curated by a panel of experts, featuring strong technical content with practical applications to current and emerging threats.

Google is excited to both sponsor and help USENIX build Enigma, since we share many of its core principles: transparency, openness, and cutting-edge security research. Furthermore, we are proud to provide Enigma with with engineering and design support, as well as volunteer participation in program and steering committees.

The first instantiation of Enigma will be held January 25-27 in San Francisco. You can sign up for more information about the conference or propose a talk through the official conference site at http://enigma.usenix.org

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

Google Computer Vision research at CVPR 2015

Posted by Vincent Vanhoucke, Google Research Scientist

Much of the world's 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)

Advances in Variational Inference: Working Towards Large-scale Probabilistic Machine Learning at NIPS 2014

Posted by Shakir Mohamed and Charles Blundell, Google DeepMind, London

At Google, we continually explore and develop large-scale machine learning systems to improve our user’s experience, such as providing better video recommendations, deciding on the best language translation in a given context, or improving the accuracy of image search results. The data used to train these systems often contains many inconsistencies and missing elements, making progress towards large-scale probabilistic models designed to address these problems an important and ongoing part of our research. One principled and efficient approach for developing such models relies on an approach known as Variational Inference.

A renewed interest and several recent advances in variational inference^1,2,3,4,5,6 has motivated us to support and co-organise this year’s workshop on Advances in Variational Inference as part of the Neural Information Processing Systems (NIPS) conference in Montreal. These advances include new methods for scalability using stochastic gradient methods, the ability to handle data that arrives continuously as a stream, inference in non-linear time-series models, principled regularisation in deep neural networks, and inference-based decision making in reinforcement learning, amongst others.

Whilst variational methods have clearly emerged as a leading approach for tractable, large-scale probabilistic inference, there remain important trade-offs in speed, accuracy, simplicity and applicability between variational and other approximative schemes. The goal of the workshop will be to contextualise these developments and address some of the many unanswered questions through:

• Contributed talks from 6 speakers who are leading the resurgence of variational inference, and shaping the debate on topics of stochastic optimisation, deep learning, Bayesian non-parametrics, and theory.
• 34 contributed papers covering significant advances in methodology, theory and applications including efficient optimisation, streaming data analysis, submodularity, non-parametric modelling and message passing.
• A panel discussion with leading researchers in the field that will further interrogate these ideas. Our panelists are David Blei, Neil Lawrence, Shinichi Nakajima and Matthias Seeger.

The workshop presents a fantastic opportunity to discuss the opportunities and obstacles facing the wider adoption of variational methods. The workshop will be held on the 13th December 2014 at the Montreal Convention and Exhibition Centre. For more details see: www.variationalinference.org.

References:

1. Rezende, Danilo J., Shakir Mohamed, and Daan Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models, Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.

2. Gregor, Karol, Ivo Danihelka, Andriy Mnih, Charles Blundell and Daan Wierstra, Deep AutoRegressive Networks, Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.

3. Mnih, Andriy, and Karol Gregor, Neural Variational Inference and Learning in Belief Networks, Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.

4. Kingma, D. P. and Welling, M., Auto-Encoding Variational Bayes, Proceedings of the International Conference on Learning Representations (ICLR), 2014.

5. Broderick, T., Boyd, N., Wibisono, A., Wilson, A. C., & Jordan, M., Streaming Variational Bayes, Advances in Neural Information Processing Systems (pp. 1727-1735), 2013.

6. Hoffman, M., Blei, D. M., Wang, C., and Paisley, J., Stochastic Variational Inference, Journal of Machine Learning Research, 14:1303–1347, 2013.

The World Parks Congress: Using technology to protect our natural environment

Posted by Dave Thau, Developer Advocate for Google Earth Engine and Karin Tuxen-Bettman, Program Manager, Google Earth Outreach

(Cross posted on the Official Google Australia Blog)

This week, thousands of people from more than 160 countries will gather in Sydney for the once-in-a-decade IUCN World Parks Congress to discuss the governance and management of protected areas. The Google Earth Outreach and Google Earth Engine teams will be at the event to showcase exemplars of how technology can help protect our environment.

Here are a few of the workshops and events happening in Sydney this week:

• Monday, November 10th - Tuesday, November 11th: Over the last couple of days, the Google Earth Outreach and Earth Engine teams delivered a 2-day hands-on workshop to develop the technical capacity of park managers, researchers, and communities. At this workshop, participants were introduced to Google mapping tools to help them with their conservation programs. 
• November 13 - 19: Google will be at the Oceans Pavilion inside the World Parks Congress to demonstrate how Trekker, Street View and Open Data Kit on Android mobile devices can assist with parks monitoring and management. 
• Friday, November 14, 9:30-10:30am: Join a Live Sydney Seahorse Hunt in Sydney Harbour, via Google Hangout, with Catlin Seaview Survey and Sydney Institute of Marine Science. Richard Vevers, Director of the Catlin Seaview Survey, will venture underwater to his favorite dive site and talk with experts about the unique marine life (including seahorses!) that explorers can expect to find around Sydney. Tune in here at 10:30am to catch all the action. 
• Saturday, November 15th, 8:30am: Networking for nature: the future is cool. Hear about how technology-driven ocean initiatives can help us better understand and strengthen our connection with our natural environments. WPCA-Marine’s plenary session will includes presentations by Sylvia Earle and Mission Blue, Catlin Seaview Survey, Google, Oceana, and SkyTruth. The session will also feature leading young marine professionals Mariasole Bianco and Rebecca Koss. 
• Saturday, November 15th, 12:15pm: We’ll be hosting a panel discussion on using Global Forest Watch to monitor protected areas in near-real-time. Global Forest Watch is a dynamic online alert system to help park rangers monitor and preserve vast stretches of parkland.
• Saturday, November 15th, 1:30 - 3:00pm: At the Biodiversity Pavilion join Walter Jetz from Yale and Dave Thau from Google for a presentation on Google Earth Engine and The Map of Life. The presentation will showcase how Google Earth Engine is being used in a variety of conservations efforts - including monitoring water resources, the health of the world's forests, and measuring the impact of protected areas on biodiversity preservation. We will also announce a new global resource from The Map of Life for mapping and monitoring biodiverse ecosystems. 

We believe that technology can help address some of our world’s most pressing environmental challenges and we look forward to working with Australian conservationists to integrate technology into their work.

You can find us at the Oceans Pavilion inside the World Parks Congress, where we will be joined by our environmental partners including The Jane Goodall Institute, The World Resources Institute and The Map of Life.

We hope to see you at one of our events this week!

All the News that's Fit to Read: A Study of Social Annotations for News Reading

Posted by Chinmay Kulkarni, Stanford University Ph.D candidate and former Google Intern, and Ed H. Chi, Google Research Scientist

News is one of the most important parts of our collective information diet, and like any other activity on the Web, online news reading is fast becoming a social experience. Internet users today see recommendations for news from a variety of sources; newspaper websites allow readers to recommend news articles to each other, restaurant review sites present other diners’ recommendations, and now several social networks have integrated social news readers.

With news article recommendations and endorsements coming from a combination of computers and algorithms, companies that publish and aggregate content, friends and even complete strangers, how do these explanations (i.e. why the articles are shown to you, which we call “annotations”) affect users' selections of what to read? Given the ubiquity of online social annotations in news dissemination, it is surprising how little is known about how users respond to these annotations, and how to offer them to users productively.

In All the News that’s Fit to Read: A Study of Social Annotations for News Reading, presented at the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems and highlighted in the list of influential Google papers from 2013, we reported on results from two experiments with voluntary participants that suggest that social annotations, which have so far been considered as a generic simple method to increase user engagement, are not simple at all; social annotations vary significantly in their degree of persuasiveness, and their ability to change user engagement.

News articles in different annotation conditions

The first experiment looked at how people use annotations when the content they see is not personalized, and the annotations are not from people in their social network, as is the case when a user is not signed into a particular social network. Participants who signed up for the study were suggested the same set of news articles via annotations from strangers, a computer agent, and a fictional branded company. Additionally, they were told whether or not other participants in the experiment would see their name displayed next to articles they read (i.e. “Recorded” or “Not Recorded”).

Surprisingly, annotations by unknown companies and computers were significantly more persuasive than those by strangers in this “signed-out” context. This result implies the potential power of suggestion offered by annotations, even when they’re conferred by brands or recommendation algorithms previously unknown to the users, and that annotations by computers and companies may be valuable in a signed-out context. Furthermore, the experiment showed that with “recording” on, the overall number of articles clicked decreased compared to participants without “recording,” regardless of the type of annotation, suggesting that subjects were cognizant of how they appear to other users in social reading apps.

If annotations by strangers is not as persuasive as those by computers or brands, as the first experiment showed, what about the effects of friend annotations? The second experiment examined the signed-in experience (with Googlers as subjects) and how they reacted to social annotations from friends, investigating whether personalized endorsements help people discover and select what might be more interesting content.

Perhaps not entirely surprising, results showed that friend annotations are persuasive and improve user satisfaction of news article selections. What’s interesting is that, in post-experiment interviews, we found that annotations influenced whether participants read articles primarily in three cases: first, when the annotator was above a threshold of social closeness; second, when the annotator had subject expertise related to the news article; and third, when the annotation provided additional context to the recommended article. This suggests that social context and personalized annotation work together to improve user experience overall.

Some questions for future research include whether or not highlighting expertise in annotations help, if the threshold for social proximity can be algorithmically determined, and if aggregating annotations (e.g. “110 people liked this”) help increases engagement. We look forward to further research that enable social recommenders to offer appropriate explanations for why users should pay attention, and reveal more nuances based on the presentation of annotations.