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The Thesis Review Podcast

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.

Episodes

• [34] Sasha Rush | Lagrangian Relaxation for Natural Language Decoding
  
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• [33] Michael R. Douglas | G/H Conformal Field Theory
  
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• [32] Andre Martins | The Geometry of Constrained Structured Prediction
  
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• [31] Jay McClelland | Preliminary Letter Identification in the Perception of Words and Nonwords
  
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• [30] Dustin Tran | Probabilistic Programming for Deep Learning
  
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• [29] Tengyu Ma | Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding
  
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• [28] Karen Ullrich | A Coding Perspective on Deep Latent Variable Models
  
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• [27] Danqi Chen | Neural Reading Comprehension and Beyond
  
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• [26] Kevin Ellis | Algorithms for Learning to Induce Programs
  
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• [25] Tomas Mikolov | Statistical Language Models Based on Neural Networks
  
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• [24] Martin Arjovsky | Out of Distribution Generalization in Machine Learning
  
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• [23] Simon Du | Gradient Descent for Non-convex Problems in Modern Machine Learning
  
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• [22] Graham Neubig | Unsupervised Learning of Lexical Information for Language Processing Systems
  
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• [21] Michela Paganini | Machine Learning Solutions for High Energy Physics
  
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• [20] Josef Urban | Exploring and Combining Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics
  
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• [19] Dumitru Erhan | Understanding Deep Architectures and the Effect of Unsupervised Pretraining
  
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• [18] Eero Simoncelli | Distributed Representation & Analysis of Visual Motion
  
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• [17] Paul Middlebrooks | Neuronal Correlates of Metacognition in Primate Frontal Cortex
  
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• [16] Aaron Courville | A Latent Cause Theory of Classical Conditioning
  
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• [15] Christian Szegedy | Some Applications of the Weighted Combinatorial Laplacian
  
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• [14] Been Kim | Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
  
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• [13] Adji Bousso Dieng | Deep Probabilistic Graphical Modeling
  
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• [12] Martha White | Regularized Factor Models
  
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• [11] Jacob Andreas | Learning from Language
  
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• [10] Chelsea Finn | Learning to Learn with Gradients
  
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• [09] Kenneth Stanley | Efficient Evolution of Neural Networks through Complexification
  
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• [08] He He | Sequential Decisions and Predictions in Natural Language Processing
  
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• [07] John Schulman | Optimizing Expectations: From Deep RL to Stochastic Computation Graphs
  
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• [06] Yoon Kim | Deep Latent Variable Models of Natural Language
  
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• [05] Julian Togelius | Optimization, Imitation, and Innovation: Computational Intelligence and Games
  
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• [04] Sebastian Nowozin | Learning with Structured Data: Applications to Computer Vision
  
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• [03] Sebastian Ruder | Neural Transfer Learning for Natural Language Processing
  
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• [02] Colin Raffel | Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching
  
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• [01] Gus Xia | Expressive Collaborative Music Performance via Machine Learning
  
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• [00] Introduction | The Thesis Review Podcast
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