- talks - lists.cs.princeton.edu

Colloquium Speaker: Ellie Pavlick, Today- 12:30pm
by Nicole E. Wagenblast 03 Apr '17

03 Apr '17

Colloquium Speaker Ellie Pavlick , University of Pennsylvania Monday, April 3, 2017 - 12:30pm Computer Science 105 Prof. Elad Hazan Natural Language Understanding with Paraphrases and Composition Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri. As AI is applied to increasingly complex domains such as health care, education, and government, NLP will play a crucial role in allowing computational systems to access the vast amount of human knowledge documented in the form of unstructured speech and text. In this talk, I will discuss my work on training computers to make inferences about what is true or false based on information expressed in natural language. My approach combines machine learning with insights from formal linguistics in order to build data-driven models of semantics which are more precise and interpretable than would be possible using linguistically naive approaches. I will begin with my work on automatically adding semantic annotations to the 100 million phrase pairs in the Paraphrase Database (PPDB). These annotations provide the type of information necessary for carrying out precise inferences in natural language, transforming the database into a largest available lexical semantics resource for natural language processing. I will then turn to the problem of compositional entailment, and present an algorithm for performing inferences about long phrases which are unlikely to have been observed in data. Finally, I will discuss my current work on pragmatic reasoning: when and how humans derive meaning from a sentence beyond what is literally contained in the words. I will describe the difficulties that such "common-sense" inference poses for automatic language understanding, and present my on-going work on models for overcoming these challenges. Ellie Pavlick is a PhD student at the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her dissertation focuses on natural language inference and entailment. Outside of her dissertation research, Ellie has published work on stylistic variation in paraphrase--e.g. how paraphrases can effect the formality or the complexity of language--and on applications of crowdsourcing to natural language processing and social science problems. She has been involved in the design and instruction of Penn's first undergraduate course on Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen Institute for Artificial Intelligence.

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Colloquium Speaker: Song Han- Monday, April 3- 4:30pm- reminder
by Nicole E. Wagenblast 02 Apr '17

02 Apr '17

Computer Science and Electrical Engineering Colloquium Speaker Song Han, Stanford University Monday, April 3, 4:30pm Computer Science 105 Host: Prof. Kai Li and Prof. David Wentzlaff Efficient Methods and Hardware for Deep Learning Deep learning has spawned a wide range of AI applications that are changing our lives. However, deep neural networks are both computationally and memory intensive, thus they are power hungry when deployed on embedded systems and data centers with a limited power budget. To address this problem, I will present an algorithm and hardware co-design methodology for improving the efficiency of deep learning. I will first introduce "Deep Compression", which can compress deep neural network models by 10–49× without loss of prediction accuracy for a broad range of CNN, RNN, and LSTMs. The compression reduces both computation and storage. Next, by changing the hardware architecture and efficiently implementing Deep Compression, I will introduce EIE, the Efficient Inference Engine, which can perform decompression and inference simultaneously, saving a significant amount of memory bandwidth. By taking advantage of the compressed model and being able to deal with an irregular computation pattern efficiently, EIE achieves 13× speedup and 3000× better energy efficiency over GPU. Finally, I will revisit the inefficiencies in current learning algorithms, present DSD training, and discuss the challenges and future work in efficient methods and hardware for deep learning. Song Han is a Ph.D. candidate supervised by Prof. Bill Dally at Stanford University. His research focuses on energy-efficient deep learning, at the intersection between machine learning and computer architecture. He proposed the Deep Compression algorithm, which can compress neural networks by 10–49× while fully preserving prediction accuracy. He designed the first hardware accelerator that can perform inference directly on a compressed sparse model, which results in significant speedup and energy saving. His work has been featured by O’Reilly, TechEmergence, TheNextPlatform, and Embedded Vision, and it has impacted the industry. He led research efforts in model compression and hardware acceleration that won the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17. Before joining Stanford, Song graduated from Tsinghua University.

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Colloquium Speaker: Ellie Pavlick, Monday, April 3- 12:30pm- reminder
by Nicole E. Wagenblast 02 Apr '17

02 Apr '17

Colloquium Speaker Ellie Pavlick , University of Pennsylvania Monday, April 3, 2017 - 12:30pm Computer Science 105 Prof. Elad Hazan Natural Language Understanding with Paraphrases and Composition Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri. As AI is applied to increasingly complex domains such as health care, education, and government, NLP will play a crucial role in allowing computational systems to access the vast amount of human knowledge documented in the form of unstructured speech and text. In this talk, I will discuss my work on training computers to make inferences about what is true or false based on information expressed in natural language. My approach combines machine learning with insights from formal linguistics in order to build data-driven models of semantics which are more precise and interpretable than would be possible using linguistically naive approaches. I will begin with my work on automatically adding semantic annotations to the 100 million phrase pairs in the Paraphrase Database (PPDB). These annotations provide the type of information necessary for carrying out precise inferences in natural language, transforming the database into a largest available lexical semantics resource for natural language processing. I will then turn to the problem of compositional entailment, and present an algorithm for performing inferences about long phrases which are unlikely to have been observed in data. Finally, I will discuss my current work on pragmatic reasoning: when and how humans derive meaning from a sentence beyond what is literally contained in the words. I will describe the difficulties that such "common-sense" inference poses for automatic language understanding, and present my on-going work on models for overcoming these challenges. Ellie Pavlick is a PhD student at the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her dissertation focuses on natural language inference and entailment. Outside of her dissertation research, Ellie has published work on stylistic variation in paraphrase--e.g. how paraphrases can effect the formality or the complexity of language--and on applications of crowdsourcing to natural language processing and social science problems. She has been involved in the design and instruction of Penn's first undergraduate course on Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen Institute for Artificial Intelligence.

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Colloquium Speaker: Ellie Pavlick, Monday, April 3- 12:30pm
by Nicole E. Wagenblast 31 Mar '17

31 Mar '17

Colloquium Speaker Ellie Pavlick , University of Pennsylvania Monday, April 3, 2017 - 12:30pm Computer Science 105 Prof. Elad Hazan Natural Language Understanding with Paraphrases and Composition Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri. As AI is applied to increasingly complex domains such as health care, education, and government, NLP will play a crucial role in allowing computational systems to access the vast amount of human knowledge documented in the form of unstructured speech and text. In this talk, I will discuss my work on training computers to make inferences about what is true or false based on information expressed in natural language. My approach combines machine learning with insights from formal linguistics in order to build data-driven models of semantics which are more precise and interpretable than would be possible using linguistically naive approaches. I will begin with my work on automatically adding semantic annotations to the 100 million phrase pairs in the Paraphrase Database (PPDB). These annotations provide the type of information necessary for carrying out precise inferences in natural language, transforming the database into a largest available lexical semantics resource for natural language processing. I will then turn to the problem of compositional entailment, and present an algorithm for performing inferences about long phrases which are unlikely to have been observed in data. Finally, I will discuss my current work on pragmatic reasoning: when and how humans derive meaning from a sentence beyond what is literally contained in the words. I will describe the difficulties that such "common-sense" inference poses for automatic language understanding, and present my on-going work on models for overcoming these challenges. Ellie Pavlick is a PhD student at the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her dissertation focuses on natural language inference and entailment. Outside of her dissertation research, Ellie has published work on stylistic variation in paraphrase--e.g. how paraphrases can effect the formality or the complexity of language--and on applications of crowdsourcing to natural language processing and social science problems. She has been involved in the design and instruction of Penn's first undergraduate course on Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen Institute for Artificial Intelligence.

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12:30-1:30pm Thu Apr 6 talk on cyber-physical systems
by Jennifer Rexford 31 Mar '17

31 Mar '17

A systems lunch talk (not a faculty candidate) next Thursday... Speaker: Tam Vu, University of Colorado at Boulder Title: Wearable and Wireless Cyber-Physical Systems for Non-invasive Sleep Quality Monitoring Date/time: 12:30-1:30pm Thu Apr 6 (with lunch starting at noon) Room: Computer Science 302 Abstract: Sleep occupies nearly a third of human’s life and acts as a critical daily function, helping our body to balance and regulate vital systems. However, in the United States, more than 35% of the population is sleep deprived. Therefore, quantifying sleep quality is important and has significant clinical value in detecting and diagnosing various sleep-related disorders. Unfortunately, the current “gold standard” for studying patients’ sleep is obtrusive, expensive, and often inaccurate. In this talk, I will introduce our wearable and radio-based sensing systems that promise unobtrusive, low-cost, and accurate sleep study for in-hospital and in-home settings. I will start with WiSpiro, a sensing system that is able to unobtrusively monitor breathing volume and detect sleep disorder breathing in patients using radio signals from afar. I will then discuss LIBS, an in-ear wearable sensing system that can simultaneously monitor human’s brain activities, eye movement, and facial movement, which are critical for fine-grained sleep stage monitoring. I will also identify other potential uses of these systems in a broader context of health care, such as monitoring eating habits and disorders, detecting autism at early stages, improving neurological surgery practice, and detecting seizure. I will conclude the talk by discussing my on-going research as well as my future directions to improve current health care practices through the development of other innovative cyber-physical healthcare systems. Bio: Tam Vu is directing Mobile and Networked Systems (MNS) Lab at University of Colorado Boulder, where he and his team work on building systems to improve pediatric health care practices. At MNS, he designs and implements novel and practical cyber-physical systems to make physiological sensing (e.g. breathing volume measurement, brainwave signal monitoring, muscle movement recording, and sleep quality monitoring) less obtrusive at lower cost. Tam Vu’s research contribution has been recognized with four best paper awards from ACM SenSys 2016, MobiCom S3 2016, MobiCom 2012, and MobiCom 2011; a Google Faculty Research Award in 2014; and wide press coverage including Denver Post, CNN TV, NY Times, The Wall Street Journal, National Public Radio (NPR), MIT Technology Review, Yahoo News. He is actively pushing his research outcome to practice through technology transfer activities with 9 patents filed in the last 3 years and forming 2 startups to commercialize them. He received his Ph.D. in Computer Science from WINLAB, Rutgers University in 2013 and a BS in Computer Science from Hanoi University of Technology in 2006.

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Colloquium Speaker: David Held, Today, 12:30pm
by Nicole E. Wagenblast 30 Mar '17

30 Mar '17

Colloquium Speaker Dr. David Held, University of California, Berkeley Thursday, March 30- 12:30pm Computer Science 105 Host: Prof. Tom Funkhouser Robots in Clutter: Learning to Understand Environmental Changes Robots today are confined to operate in relatively simple, controlled environments. One reason for this is that current methods for processing visual data tend to break down when faced with occlusions, viewpoint changes, poor lighting, and other challenging but common situations that occur when robots are placed in the real world. I will show that we can train robots to handle these variations by modeling the causes behind visual appearance changes. If robots can learn how the world changes over time, they can be robust to the types of changes that objects often undergo. I demonstrate this idea in the context of autonomous driving, and I will show how we can use this idea to improve performance for every step of the robotic perception pipeline: object segmentation, tracking, and velocity estimation. I will also present some recent work on learning to manipulate objects, using a similar framework of learning environmental changes. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives. David Held is a post-doctoral researcher at U.C. Berkeley working with Pieter Abbeel on deep reinforcement learning for robotics. He recently completed his Ph.D. in Computer Science at Stanford University with Sebastian Thrun and Silvio Savarese, where he developed methods for perception for autonomous vehicles. David has also worked as an intern on Google’s self-driving car team. Before Stanford, David was a researcher at the Weizmann Institute, where he worked on building a robotic octopus. He received a B.S. and M.S. in Mechanical Engineering at MIT and an M.S. in Computer Science at Stanford, for which he was awarded the Best Master's Thesis Award from the Computer Science Department.

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Colloquium Speaker: Song Han- Monday, April 3- 4:30pm
by Nicole E. Wagenblast 29 Mar '17

29 Mar '17

Computer Science and Electrical Engineering Colloquium Speaker Song Han, Stanford University Monday, April 3, 4:30pm Computer Science 105 Host: Prof. Kai Li and Prof. David Wentzlaff Efficient Methods and Hardware for Deep Learning Deep learning has spawned a wide range of AI applications that are changing our lives. However, deep neural networks are both computationally and memory intensive, thus they are power hungry when deployed on embedded systems and data centers with a limited power budget. To address this problem, I will present an algorithm and hardware co-design methodology for improving the efficiency of deep learning. I will first introduce "Deep Compression", which can compress deep neural network models by 10–49× without loss of prediction accuracy for a broad range of CNN, RNN, and LSTMs. The compression reduces both computation and storage. Next, by changing the hardware architecture and efficiently implementing Deep Compression, I will introduce EIE, the Efficient Inference Engine, which can perform decompression and inference simultaneously, saving a significant amount of memory bandwidth. By taking advantage of the compressed model and being able to deal with an irregular computation pattern efficiently, EIE achieves 13× speedup and 3000× better energy efficiency over GPU. Finally, I will revisit the inefficiencies in current learning algorithms, present DSD training, and discuss the challenges and future work in efficient methods and hardware for deep learning. Song Han is a Ph.D. candidate supervised by Prof. Bill Dally at Stanford University. His research focuses on energy-efficient deep learning, at the intersection between machine learning and computer architecture. He proposed the Deep Compression algorithm, which can compress neural networks by 10–49× while fully preserving prediction accuracy. He designed the first hardware accelerator that can perform inference directly on a compressed sparse model, which results in significant speedup and energy saving. His work has been featured by O’Reilly, TechEmergence, TheNextPlatform, and Embedded Vision, and it has impacted the industry. He led research efforts in model compression and hardware acceleration that won the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17. Before joining Stanford, Song graduated from Tsinghua University.

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Colloquium Speaker: David Held, Thursday March 29, 12:30pm- reminder
by Nicole E. Wagenblast 29 Mar '17

29 Mar '17

Colloquium Speaker Dr. David Held, University of California, Berkeley Thursday, March 30- 12:30pm Computer Science 105 Host: Prof. Tom Funkhouser Robots in Clutter: Learning to Understand Environmental Changes Robots today are confined to operate in relatively simple, controlled environments. One reason for this is that current methods for processing visual data tend to break down when faced with occlusions, viewpoint changes, poor lighting, and other challenging but common situations that occur when robots are placed in the real world. I will show that we can train robots to handle these variations by modeling the causes behind visual appearance changes. If robots can learn how the world changes over time, they can be robust to the types of changes that objects often undergo. I demonstrate this idea in the context of autonomous driving, and I will show how we can use this idea to improve performance for every step of the robotic perception pipeline: object segmentation, tracking, and velocity estimation. I will also present some recent work on learning to manipulate objects, using a similar framework of learning environmental changes. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives. David Held is a post-doctoral researcher at U.C. Berkeley working with Pieter Abbeel on deep reinforcement learning for robotics. He recently completed his Ph.D. in Computer Science at Stanford University with Sebastian Thrun and Silvio Savarese, where he developed methods for perception for autonomous vehicles. David has also worked as an intern on Google’s self-driving car team. Before Stanford, David was a researcher at the Weizmann Institute, where he worked on building a robotic octopus. He received a B.S. and M.S. in Mechanical Engineering at MIT and an M.S. in Computer Science at Stanford, for which he was awarded the Best Master's Thesis Award from the Computer Science Department.

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Colloquium Speaker: Ali José Mashtizadeh, Today- 12:30pm
by Nicole E. Wagenblast 28 Mar '17

28 Mar '17

Colloquium Speaker Ali José Mashtizadeh, Stanford University Tuesday, March 28- 12:30pm Computer Science 105 Host: Prof. Michael Freedman Systems and Tools for Reliable Software: Replication, Reproducibility, and Security The past decade has seen a rapid acceleration in the development of new and transformative applications in many areas including transportation, medicine, ﬁnance, and communication. Most of these applications are made possible by the increasing diversity and scale of hardware and software systems. While this brings unprecedented opportunity, it also increases the probability of failures and the difﬁculty of diagnosing them. Increased scale and transience has also made management increasingly challenging. Devices can come and go for a variety of reasons including mobility, failure and recovery, and scaling capacity to meet demand. In this talk, I will be presenting several systems that I built to address the resulting challenges to reliability, management, and security. Ori is a reliable distributed ﬁle system for devices at the network edge. Ori automates many of the tasks of storage reliability and recovery through replication, taking advantage of fast LANs and low cost local storage in edge networks. Castor is record/replay system for multi-core applications with predictable and consistently low overheads. This makes it practical to leave record/replay on in production systems, to reproduce difficult bugs when they occur, and to support recovering from hardware failures through fault tolerance. Cryptographic CFI (CCFI) is a dynamic approach to control flow integrity. Unlike previous CFI systems that rely purely on static analysis, CCFI can classify pointers based on dynamic and runtime characteristics. This limits the attacks to only actively used code paths, resulting in a substantially smaller attack surface. Ali is currently completing his PhD at Stanford University where he is advised by Prof. David Mazières. His work focuses on improving reliability, ease of management and security in operating systems and distributed systems. Previously, he was a Staff Engineer at VMware, Inc. where he was the technical lead for the live migration products. Ali received an M.Eng. in electrical engineering and computer science and a B.S. in electrical engineering from the Massachusetts Institute of Technology.

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Bogdan Pasaniuc Special Seminar - tomorrow at LSI
by Nicole E. Wagenblast 27 Mar '17

27 Mar '17

Please note, special seminar at LSI tomorrow! Tuesday, March 28 th at 10:00 a.m. in Icahn 280 Special Seminar with Bogdan Pasaniuc, Ph.D., University of California Los Angeles “ Integrating eQTL and GWAS to identify genes associated to complex traits". Abstract: Many complex traits and diseases share a correlation at a phenotypic level. Such correlations can be attributed to shared environmental or genetic architectures. Quantifying the correlation in phenotypes that is due to genetics is of great interest in understanding the causal relationship between traits. Here I present new methods for estimation of local genetic variance/covariance and discuss their relationship with the recently proposed methods for gene expression prediction as a tool to integrate eQTL and GWAS to identify genes associated to complex traits.

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