Each episode of The Thesis Review is a conversation centered around a researcher's PhD thesis, giving insight into their history, revisiting older ideas, and providing a valuable perspective on how their research has evolved (or stayed the same) since.
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[48] Tianqi Chen - Scalable and Intelligent Learning Systems
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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…
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[47] Niloofar Mireshghallah - Auditing and Mitigating Safety Risks in Large Language Models
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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 Safe…
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[46] Yulia Tsvetkov - Linguistic Knowledge in Data-Driven NLP
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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 get…
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[45] Luke Zettlemoyer - Learning to Map Sentences to Logical Form
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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 h…
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[44] Hady Elsahar - NLG from Structured Knowledge Bases (& Controlling LMs)
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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…
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[43] Swarat Chaudhuri - Logics and Algorithms for Software Model Checking
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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…
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[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields
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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…
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[41] Talia Ringer - Proof Repair
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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 …
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[40] Lisa Lee - Learning Embodied Agents with Scalably-Supervised RL
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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 re…
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[39] Burr Settles - Curious Machines: Active Learning with Structured Instances
1:06:33
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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…
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[38] Andrew Lampinen - A Computational Framework for Learning and Transforming Task Representations
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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 ar…
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[37] Joonkoo Park - Neural Substrates of Visual Word and Number Processing
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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 tit…
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[36] Dieuwke Hupkes - Hierarchy and Interpretability in Neural Models of Language Processing
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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 compositio…
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[35] Armando Solar-Lezama - Program Synthesis by Sketching
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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…
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[34] Sasha Rush - Lagrangian Relaxation for Natural Language Decoding
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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…
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[33] Michael R. Douglas - G/H Conformal Field Theory
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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 …
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[32] Andre Martins - The Geometry of Constrained Structured Prediction
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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…
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[31] Jay McClelland - Preliminary Letter Identification in the Perception of Words and Nonwords
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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 i…
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[30] Dustin Tran - Probabilistic Programming for Deep Learning
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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 probabilis…
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[29] Tengyu Ma - Non-convex Optimization for Machine Learning
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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 mac…
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[28] Karen Ullrich - A Coding Perspective on Deep Latent Variable Models
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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 …
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[27] Danqi Chen - Neural Reading Comprehension and Beyond
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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 Compre…
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[26] Kevin Ellis - Algorithms for Learning to Induce Programs
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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 inters…
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[25] Tomas Mikolov - Statistical Language Models Based on Neural Networks
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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 th…
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[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning
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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,…
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[23] Simon Du - Gradient Descent for Non-convex Problems in Modern Machine Learning
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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…
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[22] Graham Neubig - Unsupervised Learning of Lexical Information
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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…
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[21] Michela Paganini - Machine Learning Solutions for High Energy Physics
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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…
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[20] Josef Urban - Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics
1:25:18
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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 …
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[19] Dumitru Erhan - Understanding Deep Architectures and the Effect of Unsupervised Pretraining
1:20:03
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Dumitru Erhan is a Research Scientist at Google Brain. His research focuses on understanding the world with neural networks.Dumitru's PhD thesis is titled "Understanding Deep Architectures and the Effect of Unsupervised Pretraining", which he completed in 2010 at the University of Montreal. We discuss his work in the thesis on understanding deep ne…
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[18] Eero Simoncelli - Distributed Representation and Analysis of Visual Motion
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Eero Simoncelli is a Professor of Neural Science, Mathematics, Data Science, and Psychology at New York University. His research focuses on representation and analysis of visual information.Eero's PhD thesis is titled "Distributed Representation & Analysis of Visual Motion", which he completed in 1993 at MIT. We discuss his PhD work which focused o…
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[17] Paul Middlebrooks - Neuronal Correlates of Meta-Cognition in Primate Frontal Cortex
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Paul Middlebrooks is a neuroscientist and host of the Brain Inspired podcast, which explores the intersection of neuroscience and artificial intelligence.Paul's PhD thesis is titled "Neuronal Correlates of Meta-Cognition in Primate Frontal Cortex", which he completed at the University of Pittsburgh in 2011. We discuss Paul's work on meta-cognition …
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[16] Aaron Courville - A Latent Cause Theory of Classical Conditioning
1:19:21
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Aaron Courville is a Professor at the University of Montreal. His research focuses on the development of deep learning models and methods.Aaron's PhD thesis is titled "A Latent Cause Theory of Classical Conditioning", which he completed at Carnegie Mellon University in 2006. We discuss Aaron's work on the latent cause theory during his PhD, talk ab…
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[15] Christian Szegedy - Some Applications of the Weighted Combinatorial Laplacian
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Christian Szegedy is a Research Scientist at Google. His research machine learning methods such as the inception architecture, batch normalization and adversarial examples, and he currently investigates machine learning for mathematical reasoning.Christian’s PhD thesis is titled "Some Applications of the Weighted Combinatorial Laplacian" which he c…
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[14] Been Kim - Interactive and Interpretable Machine Learning Models
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Been Kim is a Research Scientist at Google Brain. Her research focuses on designing high-performance machine learning methods that make sense to humans.Been's PhD thesis is titled "Interactive and Interpretable Machine Learning Models for Human Machine Collaboration", which she completed in 2015 at MIT. We discuss her work on interpretability, incl…
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[13] Adji Bousso Dieng - Deep Probabilistic Graphical Modeling
1:07:50
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Adji Bousso Dieng is currently a Research Scientist at Google AI, and will be starting as an assistant professor at Princeton University in 2021. Her research focuses on combining probabilistic graphical modeling and deep learning to design models for structured high-dimensional data.Her PhD thesis is titled "Deep Probabilistic Graphical Modeling",…
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[12] Martha White - Regularized Factor Models
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Martha White is an Associate Professor at the University of Alberta. Her research focuses on developing reinforcement learning and representation learning techniques for adaptive, autonomous agents learning on streams of data.Her PhD thesis is titled "Regularized Factor Models", which she completed in 2014 at the University of Alberta. We discuss t…
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[11] Jacob Andreas - Learning from Language
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Jacob Andreas is an Assistant Professor at MIT, where he leads the language and intelligence group, focusing on language as a communicative and computational tool.His PhD thesis is titled "Learning from Language" which he completed in 2018 at UC Berkeley. We discuss compositionality and neural module networks, the intersection of RL and language, a…
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[10] Chelsea Finn - Learning to Learn with Gradients
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Chelsea Finn is an Assistant Professor at Stanford University, where she leads the IRIS lab that studies intelligence through robotic interaction at scale.Her PhD thesis is titled "Learning to Learn with Gradients", which she completed in 2018 at UC Berkeley. Chelsea received the prestigious ACM Doctoral Dissertation Award for her work in the thesi…
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[09] Kenneth Stanley - Efficient Evolution of Neural Networks through Complexification
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Kenneth Stanley is a researcher at OpenAI, where he leads the team on Open-endedness. Previously he was a Professor Computer Science at the University of Central Florida, cofounder of Geometric Intelligence, and head of Core AI research at Uber AI labs.His PhD thesis is titled "Efficient Evolution of Neural Networks through Complexification", which…
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[08] He He - Sequential Decisions and Predictions in NLP
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He He is an Assistant Professor at New York University. Her research focuses on enabling reliable communication in natural language between machine and humans, including topics in text generation, robust language understanding, and dialogue systems.Her PhD thesis is titled "Sequential Decisions and Predictions in NLP", which she completed in 2016 a…
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[07] John Schulman - Optimizing Expectations: From Deep RL to Stochastic Computation Graphs
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John Schulman is a Research Scientist and co-founder of Open AI. John co-leads the reinforcement learning team, researching algorithms that safely and efficiently learn by trial and error and by imitating humans.His PhD thesis is titled "Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs", which he completed …
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[06] Yoon Kim - Deep Latent Variable Models of Natural Language
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Yoon Kim is currently a Research Scientist at the MIT-IBM AI Watson Lab, and will be joining MIT as an assistant professor in 2021.Yoon’s research focuses on machine learning and natural language processing. His PhD thesis is titled "Deep Latent Variable Models of Natural Language", which he completed in 2020 at Harvard University. We discuss his w…
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[05] Julian Togelius - Computational Intelligence and Games
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Julian Togelius is an Associate Professor at New York University, where he co-directs the NYU Game Innovation Lab. His research is at the intersection of computational intelligence and computer games.His PhD thesis is titled "Optimization, Imitation, and Innovation: Computational Intelligence and Games", which he completed in 2007. We cover his wor…
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[04] Sebastian Nowozin - Learning with Structured Data: Applications to Computer Vision
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Sebastian Nowozin is currently a Researcher at Microsoft Research Cambridge. His research focuses on probabilistic deep learning, consequences of model misspecification, understanding agent complexity in order to improve learning efficiency, and designing models for reasoning and planning.His PhD thesis is titled "Learning with Structured Data: App…
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[03] Sebastian Ruder - Neural Transfer Learning for Natural Language Processing
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1:24:32
Sebastian Ruder is currently a Research Scientist at Deepmind. His research focuses on transfer learning for natural language processing, and making machine learning and NLP more accessible. His PhD thesis is titled "Neural Transfer Learning for Natural Language Processing", which he completed in 2019. We cover transfer learning from philosophical …
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[02] Colin Raffel - Learning-Based Methods for Comparing Sequences
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1:15:54
Colin Raffel is currently a Senior Research Scientist at Google Brain, and soon to be an assistant professor at the University of North Carolina. His recent work focuses on transfer learning and learning from limited labels. His thesis is titled "Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matchi…
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[01] Gus Xia - Expressive Collaborative Music Performance via Machine Learning
57:59
57:59
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57:59
Gus Xia is an assistant professor at New York University Shanghai. His research explores machine learning for music, with a goal of building intelligent systems that understand and extend musical creativity and expression.His PhD thesis is titled Expressive Collaborative Music Performance via Machine Learning, which we discuss in depth along with h…
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[00] The Thesis Review Podcast - Introduction
1:46
1:46
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1:46
[00] The Thesis Review Podcast - Introduction by Sean Welleckעל ידי Sean Welleck
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