ETHZ++ NLP Reading Group
Time: Fri 15-16h via Zoom
Reading List
Date | Presenter(s) | Topic | Title | Authors | Bib |
---|---|---|---|---|---|
15/01/20 | Bashar Alhafni | Gender-Aware Reinflection | Gender-Aware Reinflection using Linguistically Enhanced Neural Models | Bashar Alhafni, Nizar Habash, Houda Bouamor | |
11/12/19 | Alessandro | SpanBERT | SpanBERT: Improving Pre-training by Representing and Predicting Spans | Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, Omer Levy | |
04/12/19 | Rowan | Semantic Categories and Efficient Coding | Semantic categories of artifacts and animals reflect efficient coding | Noga Zaslavsky, Terry Regier, Naftali Tishby, Charles Kemp | |
27/11/19 | Clara | Mirror Descent | Optimizing with constraints: reparametrization and geometry | Vlad Niculae | |
6/11/19 | Sankalan | MC-Dropout | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning | Yarin Gal, Zoubin Ghahramani | |
30/10/19 | Marinela | Pruning | The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | Jonathan Frankle, Michael Carbin | |
23/10/19 | Afra | Sampling without Replacement | Incremental Sampling Without Replacement for Sequence Models | Kensen Shi, David Bieber, Charles Sutton | |
16/10/19 | Shehzaad | Critique of Leaderboards | Utility is in the Eye of the User: A Critique of NLP Leaderboards | Kawin Ethayarajh, Dan Jurafsky | |
9/10/19 | Jiaoda | Dropout and Pruning | Learning Sparse Networks Using Targeted Dropout | Aidan N. Gomez, Ivan Zhang, Siddhartha Rao Kamalakara, Divyam Madaan, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton | |
2/10/19 | Tiago | V-Information | A Theory of Usable Information under Computational Constraints | Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon | |
25/09/19 | Tianyu | Coreference Resolution | End-to-end Neural Coreference Resolution | Kenton Lee, Luheng He, Mike Lewis, Luke Zettlemoyer | |
18/09/19 | Eleanor | Centering Theory | Centering: A Framework for Modeling the Local Coherence of Discourse | Barbara J. Grosz, Aravind K. Joshi, Scott Weinstein | |
02/07/19 - 23/07/19 | Reinforcement Learning | Fundamentals of Reinforcement Learning | Martha White and Adam White | ||
01/05/19 | Bayesian Non-Parametrics | Bayesian density estimation and inference using mixtures Markov chain sampling methods for Dirichlet process mixture models | |||
28/04/19 | NLP Retrospective | Experience Grounds Language Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data | |||
24/04/19 | Bayesian Non-Parametrics | Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures | |||
17/04/19 | Bayesian Non-Parametrics | Lecture Notes on Bayesian Nonparametrics - Ch.6: Exchangeability | |||
15/04/19 | Probing | Information-Theoretic Probing with Minimum Description Length | |||
14/04/19 | Bayesian Non-Parametrics | Lecture Notes on Bayesian Nonparametrics - Ch.2: Clustering and the Dirichlet Process Stat 362: Bayesian Nonparametrics - Lecture 1 |