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AI@MIT club Reading Group

The AI@MIT club holds (nearly) weekly reading groups on topics in machine learning, from theory to computer vision to systems applications. Join the mailing list and Slack community to hear more + discuss!

Find paper PDFs, along with some slides and notes, in this repository.

Spring 2021 reading group schedule

Date Paper Slides Presenter
4/7/2020 Recent advances in contrastive learning slides Rumen Dangovski
3/31/2020 Online Learning and Regret Minimization slides Max Fishelson
3/24/2020 Fixing Data Augmentation to Improve Adversarial Robustness slides Kristian Georgiev
3/17/2021 Contrastive Text Generation slides Darsh Shah

Fall 2020 reading group schedule

Date Paper Slides Presenter
11/20/2020 Train simultaneously, generalize better: Stability of gradient-based minimax learners Kristian Georgiev
11/13/2020 Deep Learning for Symbolic Mathematics slides Shayda Moezzi
10/30/2020 Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time slides Farren Alet
10/23/2020 Denoising Diffusion Probabilistic Models slides Ajay Jain
10/9/2020 Robust Encodings: A Framework for Combating Adversarial Typos annotated paper Raj Movva
4/7/2020 Meta-Learning Symmetries by Reparameterization Kristian Georgiev
3/4/2020 What Do Neural Networks Learn When Trained With Random Labels? slides Kaveri Nadhamuni

Spring 2020 reading group schedule

Date Paper Slides Presenter
4/15/2020 Fractal AI: A Fragile Theory of Intelligence slides Tony Wang
4/7/2020 A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach Kristian Georgiev
3/4/2020 Invertible Residual Networks slides Kaveri Nadhamuni
2/19/2020 Towards Learned Algorithms, Data Structures, and Systems slides Prof. Tim Kraska

Fall 2019 reading group schedule

Date Paper Slides Presenter
12/04/2019 Representational Power of Graph Neural Networks slides Prof. Stefanie Jegelka
11/27/2019 On First-Order Meta-Learning Algorithms Kaveri Nadhamuni
11/20/2019 Review of speech synthesis backend: WaveNet, WaveRNN and related papers slides Kristian Georgiev
11/13/2019 Junction Tree Variational Autoencoder for Molecular Graph Generation slides Raj Movva
11/6/2019 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Robert Cunningham
10/30/2019 Learning the Depths of Moving People by Watching Frozen People slides Ethan Weber
10/23/2019 XLNet: Generalized Autoregressive Pretraining for Language Understanding slides Moin Nadeem
10/16/2019 Stabilizing the Lottery Ticket Hypothesis slides Yoni Shteibel
10/9/2019 Doing for our robots what nature did for us slides Prof. Leslie Kaelbling
10/2/2019 A New Perspective on Adversarial Perturbations Prof. Aleksander Madry
9/25/2019 Variational Autoencoders and Nonlinear ICA: A Unifying Framework Shreyas Balaji
9/18/2019 Learning Representations by Maximizing Mutual Information Across Views Alex Coventry
9/11/2019 Active Learning for Convolutional Neural Networks: A Core-Set Approach slides Kaveri Nadhamuni

Spring 2019 reading group schedule

Date Paper Slides Presenter
5/6/2019 Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments slides Rose Wang
4/29/2019 Learning Latent Permutations with Gumbel-Sinkhorn Networks slides Ajay Jain
4/19/2019 Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models slides Kaveri Nadhamuni
4/10/2019 A Brief Introduction to Hyperbolic Geometry for Machine Learning slides Justin Chen
4/3/2019 Sampling Matters in Deep Embedding Learning Samson Timoner
3/13/2019 Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation Sualeh Asif
3/6/2019 A Style-Based Generator Architecture for Generative Adversarial Networks (video) slides Abhinav Venigalla
2/25/2019 Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks slides Ajay Jain

Fall 2018 reading group schedule

Date Paper Presenter
11/28/2018 BAGAN: Data Augmentation with Balancing GAN Kaveri Nadhamuni
11/14/2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Sree Harsha Nelaturu
10/31/2018 How Does Batch Normalization Help Optimization? Moin Nadeem
10/24/2018 Annotating the World Wide Web using Natural Language, Omnibase: Uniform Access to Heterogeneous Data for Question Answering Michael Silver
10/17/2018 Learning to Compose Neural Networks for Question Answering Sree Harsha Nelaturu
10/10/2018 Neural Turing Machines (blog post) Nate Foss
10/3/2018 Bring your own paper! --
9/26/2018 Variational Inference with Normalizing Flows Ajay Jain

Spring 2018 reading group schedule

Date Paper Presenter
5/2/18 DeepCoder: Learning to Write Programs Nate Foss
4/25/18 Guest speaker: GAN overview, CycleGAN, BicycleGAN Jun-Yan Zhu (MIT CSAIL)
4/11/18 Toward Multimodal Image-to-Image Translation Tieshun Roquerre
4/4/18 Stochastic Program Optimization Ajay Jain
3/14/18 Learned Index Structures Kristian Georgiev
3/7/18 Rationalizing Neural Predictions Collaborative read
2/28/18 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Parth Shah
2/21/18 Overview: Adversarial Examples Bristy Sikder
2/14/18 Large Scale Distributed Deep Networks Ajay Jain & Justin Chen

Fall 2017 reading group schedule

Date Paper Presenter
12/7/2017 Movie screening: AlphaGo --
12/6/2017 Guest speakers: Robust Adversarial Examples Anish Athalye & Logan Engstrom (MIT)
11/29/2017 Improved Training of Wasserstein GANs Isaac Wolverton
11/15/2017 Mastering the game of Go without human knowledge (AlphaGo Zero) Jeremy Nixon
11/8/2017 Decoupled Neural Interfaces using Synthetic Gradients Andrew Luo
10/26/2017 Playing Atari with Deep Reinforcement Learning Tim Plump
10/19/2017 One/Few shot learning Matthew Feng & Parth Shah
10/12/2017 Skip thought vectors Nikhil Murthy
10/6/2017 DeepFace and FaceNet Ajay Jain
9/28/2017 DenseNet, ResNet and HighwayNet Tim Plump
9/18/2017 Guest speaker: Object detection and recognition Paras Jain (DeepScale)
9/14/2017 Feature Pyramid Networks for Object Detection Andrew Luo

Fall 2016 reading group schedule

Date Paper Presenter
11/7/2016 Adam - A method for stochastic optimization Hassan Kane
10/31/2016 Learning to Protect Communications with Adversarial Neural Cryptography Simanta Gautam
10/24/2016 Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Nick
10/17/2016 Human-level control through deep reinforcement learning Ali-Amir Aldan
10/3/2016 Generative Adversarial Networks Prafulla Dhariwal