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The Thesis Review Podcast

E-mail: thesisreviewpodcast {@ | at} gmail.com
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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
- [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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