[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning

The Thesis Review

#Science #Thesis Review #Sean Welleck

57:50

Alex (@alex_kamenca) and Carley (@carleymitus) are both members of our Action Academy Community that purchased TWO small businesses last thursday! Want To Quit Your Job In The Next 6-18 Months Through Buying Commercial Real Estate & Small Businesses? 👔🏝️ Schedule A Free 15 Minute Coaching Call With Our Team Here To Get "Unstuck" Want to know which investment strategy is best for you? Take our Free Asset-Selection Quiz Check Out Our Bestselling Book : From Passive To Passionate : How To Quit Your Job - Grow Your Wealth - And Turn Your Passions Into Profits Want A Free $100k+ Side Hustle Guide ? Follow Me As I Travel & Build: IG @brianluebben ActionAcademy.com…

4年之前 1:02:48

MP3•单集首页

Martin Arjovsky is a postdoctoral researcher at INRIA. His research focuses on generative modeling, generalization, and exploration in RL. Martin's PhD thesis is titled "Out of Distribution Generalization in Machine Learning", which he completed in 2019 at New York University. We discuss his work on the influential Wasserstein GAN early in his PhD, then discuss his thesis work on out-of-distribution generalization which focused on causal invariance and invariant risk minimization. Episode notes: https://cs.nyu.edu/~welleck/episode24.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

49集单集

[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning

The Thesis Review

published 4年之前

MP3•单集首页

49集单集

#Science #Thesis Review #Sean Welleck

所有剧集

1
[48] Tianqi Chen - Scalable and Intelligent Learning Systems 46:29

26 weeks之前46:29

46:29

Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems. Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI. - Episode notes: www.wellecks.com/thesisreview/episode48.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Follow Tianqi Chen on Twitter (@tqchenml) - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[47] Niloofar Mireshghallah - Auditing and Mitigating Safety Risks in Large Language Models 1:17:06

28 weeks之前1:17:06

1:17:06

Niloofar Mireshghallah is a postdoctoral scholar at the University of Washington. Her research focuses on privacy, natural language processing, and the societal implications of machine learning. Niloofar completed her PhD in 2023 at UC San Diego, where she was advised by Taylor Berg-Kirkpatrick. Her PhD thesis is titled "Auditing and Mitigating Safety Risks in Large Language Models." We discuss her journey into research and her work on privacy and LLMs, including how privacy is defined, common attacks and mitigations, differential privacy, and the balance between memorization and generalization. - Episode notes: www.wellecks.com/thesisreview/episode47.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[46] Yulia Tsvetkov - Linguistic Knowledge in Data-Driven NLP 59:53

2 years之前59:53

59:53

Yulia Tsvetkov is a Professor in the Allen School of Computer Science & Engineering at the University of Washington. Her research focuses on multilingual NLP, NLP for social good, and language generation. Yulia's PhD thesis is titled "Linguistic Knowledge in Data-Driven Natural Language Processing", which she completed in 2016 at CMU. We discuss getting started in research, then move to Yulia's work in the thesis that combines ideas from linguistics and natural language processing. We discuss low-resource and multilingual NLP, large language models, and great advice about research and beyond. - Episode notes: www.wellecks.com/thesisreview/episode46.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[45] Luke Zettlemoyer - Learning to Map Sentences to Logical Form 59:35

2 years之前59:35

59:35

Luke Zettlemoyer is a Professor at the University of Washington and Research Scientist at Meta. His work spans machine learning and NLP, including foundational work in large-scale self-supervised pretraining of language models. Luke's PhD thesis is titled "Learning to Map Sentences to Logical Form", which he completed in 2009 at MIT. We talk about his PhD work, the path to the foundational Elmo paper, and various topics related to large language models. - Episode notes: www.wellecks.com/thesisreview/episode45.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[44] Hady Elsahar - NLG from Structured Knowledge Bases (& Controlling LMs) 1:05:56

3 years之前1:05:56

1:05:56

Hady Elsahar is a Research Scientist at Naver Labs Europe. His research focuses on Neural Language Generation under constrained and controlled conditions. Hady's PhD was on interactions between Natural Language and Structured Knowledge bases for Data2Text Generation and Relation Extraction & Discovery, which he completed in 2019 at the Université de Lyon. We talk about his phd work and how it led to interests in multilingual and low-resource in NLP, as well as controlled generation. We dive deeper in controlling language models, including his interesting work on distributional control and energy-based models. - Episode notes: www.wellecks.com/thesisreview/episode44.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[43] Swarat Chaudhuri - Logics and Algorithms for Software Model Checking 1:06:18

3 years之前1:06:18

1:06:18

Swarat Chaudhuri is an Associate Professor at the University of Texas. His lab studies problems at the interface of programming languages, logic and formal methods, and machine learning. Swarat's PhD thesis is titled "Logics and Algorithms for Software Model Checking", which he completed in 2007 at the University of Pennsylvania. We discuss reasoning about programs, formal methods & safer machine learning systems, and the future of program synthesis & neurosymbolic programming. - Episode notes: www.wellecks.com/thesisreview/episode43.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields 1:18:01

3 years之前1:18:01

1:18:01

Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs. Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis. - Episode notes: https://cs.nyu.edu/~welleck/episode42.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[41] Talia Ringer - Proof Repair 1:19:02

3 years之前1:19:02

1:19:02

Talia Ringer is an Assistant Professor with the Programming Languages, Formal Methods, and Software Engineering group at University of Illinois Urbana-Champaign. Her research focuses on formal verification and proof engineering technologies. Talia's PhD thesis is titled "Proof Repair", which she completed in 2021 at the University of Washington. We discuss software verification and her PhD work on proof repair for maintaining verified systems, and discuss the intersection of machine learning with her work. - Episode notes: https://cs.nyu.edu/~welleck/episode41.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[40] Lisa Lee - Learning Embodied Agents with Scalably-Supervised RL 46:59

3 years之前46:59

46:59

Lisa Lee is a Research Scientist at Google Brain. Her research focuses on building AI agents that can learn and adapt like humans and animals do. Lisa's PhD thesis is titled "Learning Embodied Agents with Scalably-Supervised Reinforcement Learning", which she completed in 2021 at Carnegie Mellon University. We talk about her work in the thesis on reinforcement learning, including exploration, learning with weak supervision, and embodied agents, and cover various topics related to trends in reinforcement learning. - Episode notes: https://cs.nyu.edu/~welleck/episode40.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[39] Burr Settles - Curious Machines: Active Learning with Structured Instances 1:06:33

3 years之前1:06:33

1:06:33

Burr Settles leads the research group at Duolingo, a language-learning website and mobile app whose mission is to make language education free and accessible to everyone. Burr’s PhD thesis is titled "Curious Machines: Active Learning with Structured Instances", which he completed in 2008 at the University of Wisconsin-Madison. We talk about his work in the thesis on active learning, then chart the path to Burr’s role at DuoLingo. We discuss machine learning for education and language learning, including content, assessment, and the exciting possibilities opened by recent advancements. - Episode notes: https://cs.nyu.edu/~welleck/episode39.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[38] Andrew Lampinen - A Computational Framework for Learning and Transforming Task Representations 1:04:47

3 years之前1:04:47

1:04:47

Andrew Lampinen is a research scientist at DeepMind. His research focuses on cognitive flexibility and generalization. Andrew’s PhD thesis is titled "A Computational Framework for Learning and Transforming Task Representations", which he completed in 2020 at Stanford University. We talk about cognitive flexibility in brains and machines, centered around his work in the thesis on meta-mapping. We cover a lot of interesting ground, including complementary learning systems and memory, compositionality and systematicity, and the role of symbols in machine learning. - Episode notes: https://cs.nyu.edu/~welleck/episode38.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[37] Joonkoo Park - Neural Substrates of Visual Word and Number Processing 1:09:28

3 years之前1:09:28

1:09:28

Joonkoo Park is an Associate Professor and Honors Faculty in the Department of Psychological and Brain Sciences at UMass Amherst. He leads the Cognitive and Developmental Neuroscience Lab, focusing on understanding the developmental mechanisms and neurocognitive underpinnings of our knowledge about number and mathematics. Joonkoo’s PhD thesis is titled "Experiential Effects on the Neural Substrates of Visual Word and Number Processing", which he completed in 2011 at the University of Michigan. We talk about numerical processing in the brain, starting with nature vs. nurture, including the learned versus built-in aspects of neural architectures. We talk about the difference between word and number processing, types of numerical thinking, and symbolic vs. non-symbolic numerical processing. - Episode notes: https://cs.nyu.edu/~welleck/episode37.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[36] Dieuwke Hupkes - Hierarchy and Interpretability in Neural Models of Language Processing 1:02:26

3 years之前1:02:26

1:02:26

Dieuwke Hupkes is a Research Scientist at Facebook AI Research and the scientific manager of the Amsterdam unit of ELLIS. Dieuwke's PhD thesis is titled, "Hierarchy and Interpretability in Neural Models of Language Processing", which she completed in 2020 at the University of Amsterdam. We discuss her work on which aspects of hierarchical compositionality and syntactic structure can be learned by recurrent neural networks, how these models can serve as explanatory models of human language processing, what compositionality actually means, and a lot more. - Episode notes: https://cs.nyu.edu/~welleck/episode36.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[35] Armando Solar-Lezama - Program Synthesis by Sketching 1:15:56

3 years之前1:15:56

1:15:56

Armando Solar-Lezama is a Professor at MIT, and the Associate Director & COO of CSAIL. He leads the Computer Assisted Programming Group, focused on program synthesis. Armando’s PhD thesis is titled, "Program Synthesis by Sketching", which he completed in 2008 at UC Berkeley. We talk about program synthesis & his work on Sketch, how machine learning's role in program synthesis has evolved over time, and more. - Episode notes: https://cs.nyu.edu/~welleck/episode35.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

1
[34] Sasha Rush - Lagrangian Relaxation for Natural Language Decoding 1:08:12

4 years之前1:08:12

1:08:12

Sasha Rush is an Associate Professor at Cornell Tech and researcher at Hugging Face. His research focuses on building NLP systems that are safe, fast, and controllable. Sasha's PhD thesis is titled, "Lagrangian Relaxation for Natural Language Decoding", which he completed in 2014 at MIT. We talk about his work in the thesis on decoding in NLP, how it connects with today, and many interesting topics along the way such as the role of engineering in machine learning, breadth vs. depth, and more. - Episode notes: https://cs.nyu.edu/~welleck/episode34.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[33] Michael R. Douglas - G/H Conformal Field Theory 1:12:58

4 years之前1:12:58

1:12:58

Michael R. Douglas is a theoretical physicist and Professor at Stony Brook University, and Visiting Scholar at Harvard University. His research focuses on string theory, theoretical physics and its relations to mathematics. Michael's PhD thesis is titled, "G/H Conformal Field Theory", which he completed in 1988 at Caltech. We talk about working with Feynman, Sussman, and Hopfield during his PhD days, the superstring revolutions and string theory, and machine learning's role in the future of science and mathematics. - Episode notes: https://cs.nyu.edu/~welleck/episode33.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[32] Andre Martins - The Geometry of Constrained Structured Prediction 1:26:39

4 years之前1:26:39

1:26:39

Andre Martins is an Associate Professor at IST and VP of AI Research at Unbabel in Lisbon, Portugal. His research focuses on natural language processing and machine learning. Andre’s PhD thesis is titled, "The Geometry of Constrained Structured Prediction: Applications to Inference and Learning of Natural Language Syntax", which he completed in 2012 at Carnegie Mellon University and IST. We talk about his work in the thesis on structured prediction in NLP, and discuss connections between his thesis work on later work on sparsity, sparse communication, and more. - Episode notes: https://cs.nyu.edu/~welleck/episode32.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[31] Jay McClelland - Preliminary Letter Identification in the Perception of Words and Nonwords 1:33:51

4 years之前1:33:51

1:33:51

Jay McClelland is a Professor in the Psychology Department and Director of the Center for Mind, Brain, Computation and Technology at Stanford. His research addresses a broad range of topics in cognitive science and cognitive neuroscience, including Parallel Distributed Processing (PDP). Jay's PhD thesis is titled "Preliminary Letter Identification in the Perception of Words and Nonwords", which he completed in 1975 at University of Pennsylvania. We discuss his work in the thesis on the word superiority effect, how it led to the Integrated Activation model, the path to Parallel Distributed Processing and the connectionist revolution, and distributed vs rule-based and symbolic approaches to modeling human cognition and artificial intelligence. - Episode notes: https://cs.nyu.edu/~welleck/episode31.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[30] Dustin Tran - Probabilistic Programming for Deep Learning 1:02:50

4 years之前1:02:50

1:02:50

Dustin Tran is a research scientist at Google Brain. His research focuses on advancing science and intelligence, including areas involving probability, programs, and neural networks. Dustin’s PhD thesis is titled "Probabilistic Programming for Deep Learning", which he completed in 2020 at Columbia University. We discuss the intersection of probabilistic modeling and deep learning, including the Edward library and the novel inference algorithms and models that he developed in the thesis. - Episode notes: https://cs.nyu.edu/~welleck/episode30.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[29] Tengyu Ma - Non-convex Optimization for Machine Learning 1:17:22

4 years之前1:17:22

1:17:22

Tengyu Ma is an Assistant Professor at Stanford University. His research focuses on deep learning and its theory, as well as various topics in machine learning. Tengyu's PhD thesis is titled "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding", which he completed in 2017 at Princeton University. We discuss theory in machine learning and deep learning, including the 'all local minima are global minima' property, overparameterization, as well as perspectives that theory takes on understanding deep learning. - Episode notes: https://cs.nyu.edu/~welleck/episode29.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[28] Karen Ullrich - A Coding Perspective on Deep Latent Variable Models 1:06:20

4 years之前1:06:20

1:06:20

Karen Ullrich is a Research Scientist at FAIR. Her research focuses on the intersection of information theory and probabilistic machine learning and deep learning. Karen's PhD thesis is titled "A coding perspective on deep latent variable models", which she completed in 2020 at The University of Amsterdam. We discuss information theory & the minimum description length principle, along with her work in the thesis on compression and communication. Episode notes: https://cs.nyu.edu/~welleck/episode28.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[27] Danqi Chen - Neural Reading Comprehension and Beyond 55:43

4 years之前55:43

55:43

Danqi Chen is an assistant professor at Princeton University, co-leading the Princeton NLP Group. Her research focuses on fundamental methods for learning representations of language and knowledge, and practical systems including question answering, information extraction and conversational agents. Danqi’s PhD thesis is titled "Neural Reading Comprehension and Beyond", which she completed in 2018 at Stanford University. We discuss her work on parsing, reading comprehension and question answering. Throughout we discuss progress in NLP, fundamental challenges, and what the future holds. Episode notes: https://cs.nyu.edu/~welleck/episode27.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[26] Kevin Ellis - Algorithms for Learning to Induce Programs 1:17:49

4 years之前1:17:49

1:17:49

Kevin Ellis is an assistant professor at Cornell and currently a research scientist at Common Sense Machines. His research focuses on artificial intelligence, program synthesis, and neurosymbolic models. Kevin's PhD thesis is titled "Algorithms for Learning to Induce Programs", which he completed in 2020 at MIT. We discuss Kevin’s work at the intersection of machine learning and program induction, including inferring graphics programs from images and drawings, DreamCoder, and more. Episode notes: https://cs.nyu.edu/~welleck/episode26.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[25] Tomas Mikolov - Statistical Language Models Based on Neural Networks 1:19:17

4 years之前1:19:17

1:19:17

Tomas Mikolov is a Senior Researcher at the Czech Institute of Informatics, Robotics, and Cybernetics. His research has covered topics in natural language understanding and representation learning, including Word2Vec, and complexity. Tomas's PhD thesis is titles "Statistical Language Models Based on Neural Networks", which he completed in 2012 at the Brno University of Technology. We discuss compression and recurrent language models, the backstory behind Word2Vec, and his recent work on complexity & automata. Episode notes: https://cs.nyu.edu/~welleck/episode25.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning 1:02:48

4 years之前1:02:48

1:02:48

The Thesis Review

1
[23] Simon Du - Gradient Descent for Non-convex Problems in Modern Machine Learning 1:06:30

4 years之前1:06:30

1:06:30

Simon Shaolei Du is an Assistant Professor at the University of Washington. His research focuses on theoretical foundations of deep learning, representation learning, and reinforcement learning. Simon's PhD thesis is titled "Gradient Descent for Non-convex Problems in Modern Machine Learning", which he completed in 2019 at Carnegie Mellon University. We discuss his work related to the theory of gradient descent for challenging non-convex problems that we encounter in deep learning. We cover various topics including connections with the Neural Tangent Kernel, theory vs. practice, and future research directions. Episode notes: https://cs.nyu.edu/~welleck/episode23.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[22] Graham Neubig - Unsupervised Learning of Lexical Information 1:02:30

4 years之前1:02:30

1:02:30

Graham Neubig is an Associate Professor at Carnegie Mellon University. His research focuses on language and its role in human communication, with the goal of breaking down barriers in human-human or human-machine communication through the development of NLP technologies. Graham’s PhD thesis is titled "Unsupervised Learning of Lexical Information for Language Processing Systems", which he completed in 2012 at Kyoto University. We discuss his PhD work related to the fundamental processing units that NLP systems use to process text, including non-parametric Bayesian models, segmentation, and alignment problems, and discuss how his perspective on machine translation has evolved over time. Episode notes: http://cs.nyu.edu/~welleck/episode22.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at http://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[21] Michela Paganini - Machine Learning Solutions for High Energy Physics 1:07:43

4 years之前1:07:43

1:07:43

Michela Paganini is a Research Scientist at DeepMind. Her research focuses on investigating ways to compress and scale up neural networks. Michela's PhD thesis is titled "Machine Learning Solutions for High Energy Physics", which she completed in 2019 at Yale University. We discuss her PhD work on deep learning for high energy physics, including jet tagging and fast simulation for the ATLAS experiment at the Large Hadron Collider, and the intersection of machine learning and physics. Episode notes: https://cs.nyu.edu/~welleck/episode21.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[20] Josef Urban - Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics 1:25:18

4 years之前1:25:18

1:25:18

Josef Urban is a Principal Researcher at the Czech Institute of Informatics, Robotics, and Cybernetics. His research focuses on artificial intelligence for large-scale computer-assisted reasoning. Josef's PhD thesis is titled "Exploring and Combining Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics", which he completed in 2004 at Charles University in Prague. We discuss his PhD work on the Mizar Problems for Theorem Proving, machine learning for premise selection, and how it evolved into his recent research. Episode notes: https://cs.nyu.edu/~welleck/episode20.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview…

The Thesis Review

1
[19] Dumitru Erhan - Understanding Deep Architectures and the Effect of Unsupervised Pretraining 1:20:03

4 years之前1:20:03