The Thesis Review
By Sean Welleck
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Category: Science
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Subscribers: 19
Reviews: 0
Episodes: 49
Description
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.
| Episode | Date |
|---|---|
|
[48] Tianqi Chen - Scalable and Intelligent Learning Systems
|
Oct 28, 2024 |
|
[47] Niloofar Mireshghallah - Auditing and Mitigating Safety Risks in Large Language Models
|
Oct 15, 2024 |
|
[46] Yulia Tsvetkov - Linguistic Knowledge in Data-Driven NLP
|
Aug 12, 2023 |
|
[45] Luke Zettlemoyer - Learning to Map Sentences to Logical Form
|
Jul 25, 2023 |
|
[44] Hady Elsahar - NLG from Structured Knowledge Bases (& Controlling LMs)
|
Aug 23, 2022 |
|
[43] Swarat Chaudhuri - Logics and Algorithms for Software Model Checking
|
Jun 28, 2022 |
|
[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields
|
Apr 19, 2022 |
|
[41] Talia Ringer - Proof Repair
|
Mar 30, 2022 |
|
[40] Lisa Lee - Learning Embodied Agents with Scalably-Supervised RL
|
Mar 09, 2022 |
|
[39] Burr Settles - Curious Machines: Active Learning with Structured Instances
|
Feb 02, 2022 |
|
[38] Andrew Lampinen - A Computational Framework for Learning and Transforming Task Representations
|
Jan 08, 2022 |
|
[37] Joonkoo Park - Neural Substrates of Visual Word and Number Processing
|
Dec 21, 2021 |
|
[36] Dieuwke Hupkes - Hierarchy and Interpretability in Neural Models of Language Processing
|
Nov 30, 2021 |
|
[35] Armando Solar-Lezama - Program Synthesis by Sketching
|
Nov 06, 2021 |
|
[34] Sasha Rush - Lagrangian Relaxation for Natural Language Decoding
|
Oct 20, 2021 |
|
[33] Michael R. Douglas - G/H Conformal Field Theory
|
Oct 01, 2021 |
|
[32] Andre Martins - The Geometry of Constrained Structured Prediction
|
Sep 16, 2021 |
|
[31] Jay McClelland - Preliminary Letter Identification in the Perception of Words and Nonwords
|
Aug 29, 2021 |
|
[30] Dustin Tran - Probabilistic Programming for Deep Learning
|
Aug 14, 2021 |
|
[29] Tengyu Ma - Non-convex Optimization for Machine Learning
|
Aug 01, 2021 |
|
[28] Karen Ullrich - A Coding Perspective on Deep Latent Variable Models
|
Jul 16, 2021 |
|
[27] Danqi Chen - Neural Reading Comprehension and Beyond
|
Jul 02, 2021 |
|
[26] Kevin Ellis - Algorithms for Learning to Induce Programs
|
May 29, 2021 |
|
[25] Tomas Mikolov - Statistical Language Models Based on Neural Networks
|
May 14, 2021 |
|
[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning
|
Apr 30, 2021 |
|
[23] Simon Du - Gradient Descent for Non-convex Problems in Modern Machine Learning
|
Apr 16, 2021 |
|
[22] Graham Neubig - Unsupervised Learning of Lexical Information
|
Apr 02, 2021 |
|
[21] Michela Paganini - Machine Learning Solutions for High Energy Physics
|
Mar 19, 2021 |
|
[20] Josef Urban - Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics
|
Mar 05, 2021 |
|
[19] Dumitru Erhan - Understanding Deep Architectures and the Effect of Unsupervised Pretraining
|
Feb 19, 2021 |
|
[18] Eero Simoncelli - Distributed Representation and Analysis of Visual Motion
|
Feb 05, 2021 |
|
[17] Paul Middlebrooks - Neuronal Correlates of Meta-Cognition in Primate Frontal Cortex
|
Jan 22, 2021 |
|
[16] Aaron Courville - A Latent Cause Theory of Classical Conditioning
|
Jan 08, 2021 |
|
[15] Christian Szegedy - Some Applications of the Weighted Combinatorial Laplacian
|
Dec 22, 2020 |
|
[14] Been Kim - Interactive and Interpretable Machine Learning Models
|
Dec 10, 2020 |
|
[13] Adji Bousso Dieng - Deep Probabilistic Graphical Modeling
|
Nov 26, 2020 |
|
[12] Martha White - Regularized Factor Models
|
Nov 12, 2020 |
|
[11] Jacob Andreas - Learning from Language
|
Oct 29, 2020 |
|
[10] Chelsea Finn - Learning to Learn with Gradients
|
Oct 15, 2020 |
|
[09] Kenneth Stanley - Efficient Evolution of Neural Networks through Complexification
|
Oct 01, 2020 |
|
[08] He He - Sequential Decisions and Predictions in NLP
|
Sep 25, 2020 |
|
[07] John Schulman - Optimizing Expectations: From Deep RL to Stochastic Computation Graphs
|
Sep 11, 2020 |
|
[06] Yoon Kim - Deep Latent Variable Models of Natural Language
|
Aug 28, 2020 |
|
[05] Julian Togelius - Computational Intelligence and Games
|
Aug 14, 2020 |
|
[04] Sebastian Nowozin - Learning with Structured Data: Applications to Computer Vision
|
Jul 31, 2020 |
|
[03] Sebastian Ruder - Neural Transfer Learning for Natural Language Processing
|
Jul 17, 2020 |
|
[02] Colin Raffel - Learning-Based Methods for Comparing Sequences
|
Jul 03, 2020 |
|
[01] Gus Xia - Expressive Collaborative Music Performance via Machine Learning
|
Jun 18, 2020 |
|
[00] The Thesis Review Podcast - Introduction
|
Jun 13, 2020 |