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