Paper Reading Group

Notes for papers presented during our paper reading sessions

Papers:

1. [MOPO: Model-based Offline Policy Optimization](./MOPO/MOPO Model based Offline Policy Optimization.md)
2. [DETR: End-to-End Object Detection with Transformers](./DETR/DETR: End-to-End Object Detection with Transformers.md)
3. [Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation](./Efficient adaptation for end to end Vision Based Robotic Manipulation/Efficient adaption for end to end Vision Based Robotic Manipulation.md)
4. PipeDream: Generalized Pipeline Parallelism for DNN Training
5. [Lottery Ticket Hypothesis](./LTH/Lottery Ticket Hypothesis 72c1972c2b9547a1be108c45c99d9774.md)
6. [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies (Tal Linzen, Emmanuel Dupoux, Yoav Goldberg)](./Syntax and Structure in NLP/Syntax and Structure in NLP.md#assessing-the-ability-of-lstms-to-learn-syntax-sensitive-dependencies-tal-linzen-emmanuel-dupoux-yoav-goldberg)
7. [Designing and Interpreting Probes with Control Tasks (John Hewitt & Percy Liang)](./Syntax and Structure in NLP/Syntax and Structure in NLP.md#designing-and-interpreting-probes-with-control-tasks-john-hewitt--percy-liang)
8. [What Does BERT Learn about the Structure of Language? (Ganesh Jawahar, Benoît Sagot, Djamé Seddah)](./Syntax and Structure in NLP/Syntax and Structure in NLP.md#what-does-bert-learn-about-the-structure-of-language-ganesh-jawahar-beno%C3%AEt-sagot-djam%C3%A9-seddah)
9. [GNN](./GNN-1/Graph Neural Networks 65e8b918487d47f9b54b48d11207c8c8.md)
10. Universal Adversarial Triggers
11. [Confidence-Aware Learning for Deep Neural Networks (CRL)](./Confidence-Aware Learning/Confidence Aware Learning for Deep Neural Networks 5080dd3c7bff4d6bbd44632448b1de31.md)
12. [A How-to-Model Guide for Neuroscience](./A How-to-Model Guide for Neuroscience/A How-to-Model Guide for Neuroscience.md)
13. Neural ODEs
14. [Model based Reinforcement Learning](./MBRL/Model Based Reinforcement Learning 7b98a25f0aa8434ca36d783dbbf60ec1.md)
15. [Learning to describe scenes with programs](./Scenes with Programs/LEARNING TO DESCRIBE SCENES WITH PROGRAMS.md)
16. [DeepSynth: Automata Synthesis for Automatic Task Segmentation in RL](./Automata Synthesis for RL/DeepSynth Automata Synthesis for Automatic Task Se 956bdaa7e08c423aa97f319b469790d3.md)
17. [Model free conventions in MARL with Heterogeneous Preferences](./Model free conventions in MARL with Heterogeneous Preferences/Model free conventions in MARL with Heterogeneous Preferences.md)
18. [Accelerating Reinforcement Learning with Learned Skill Priors](./Accelerating Reinforcement Learning with Learned Skill Priors/Accelerating Reinforcement Learning with Learned S 55f74821b841411e9b7695dd6cab9440.md)
19. [Progressive Domain Adaptation for Object Detection](./Progressive Domain Adaptation for Object Detection/Progressive Domain Adaptation for Object Detection 478798eacb8b477e92629c95ae306a49.md)
20. [Convolutional Networks with Adaptive inference Graphs](./Convolutional Networks with Adaptive Inference Graph/Convolutional Networks with Adaptive Inference Gra 5c731fbfed5b4f93b6b49bc30f331d18.md)