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The Thesis Review Podcast

E-mail: thesisreviewpodcast {@ | at} gmail.com
Links:
[apple podcasts]
[spotify]
[soundcloud]
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Support: [patreon]
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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
- [23] Simon Du | Gradient Descent for Non-convex Problems in Modern Machine Learning
[episode notes]
- [22] Graham Neubig | Unsupervised Learning of Lexical Information for Language Processing Systems
[episode notes]
- [21] Michela Paganini | Machine Learning Solutions for High Energy Physics
[episode notes]
- [20] Josef Urban | Exploring and Combining Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics
[episode notes]
- [19] Dumitru Erhan | Understanding Deep Architectures and the Effect of Unsupervised Pretraining
[episode notes]
- [18] Eero Simoncelli | Distributed Representation & Analysis of Visual Motion
[episode notes]
- [17] Paul Middlebrooks | Neuronal Correlates of Metacognition in Primate Frontal Cortex
[episode notes]
- [16] Aaron Courville | A Latent Cause Theory of Classical Conditioning
[episode notes]
- [15] Christian Szegedy | Some Applications of the Weighted Combinatorial Laplacian
[episode notes]
- [14] Been Kim | Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
[episode notes]
- [13] Adji Bousso Dieng | Deep Probabilistic Graphical Modeling
[episode notes]
- [12] Martha White | Regularized Factor Models
[episode notes]
- [11] Jacob Andreas | Learning from Language
[episode notes]
- [10] Chelsea Finn | Learning to Learn with Gradients
[episode notes]
- [09] Kenneth Stanley | Efficient Evolution of Neural Networks through Complexification
[episode notes]
- [08] He He | Sequential Decisions and Predictions in Natural Language Processing
[episode notes]
- [07] John Schulman | Optimizing Expectations: From Deep RL to Stochastic Computation Graphs
[episode notes]
- [06] Yoon Kim | Deep Latent Variable Models of Natural Language
[episode notes]
- [05] Julian Togelius | Optimization, Imitation, and Innovation: Computational Intelligence and Games
[episode notes]
- [04] Sebastian Nowozin | Learning with Structured Data: Applications to Computer Vision
[episode notes]
- [03] Sebastian Ruder | Neural Transfer Learning for Natural Language Processing
[episode notes]
- [02] Colin Raffel | Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching
[episode notes]
- [01] Gus Xia | Expressive Collaborative Music Performance via Machine Learning
[episode notes]
- [00] Introduction | The Thesis Review Podcast
[episode notes]