6.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Meta (United Kingdom)GB158 papers
- Meta (Israel)IL62 papers
- Menlo SchoolUS51 papers
- New York UniversityUS49 papers
- Carnegie Mellon UniversityUS33 papers
- University of California, BerkeleyUS31 papers
- The University of Texas at AustinUS28 papers
- Google (United States)US27 papers
- Georgia Institute of TechnologyUS26 papers
- Stanford UniversityUS25 papers
- Cornell UniversityUS24 papers
- University of WashingtonUS20 papers
27 papers · 2 filters
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim, Peter Y. Lu, Srijon Mukherjee +4
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the trai…
Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun +1
Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores inf…
Zero-Shot Recognition via Optimal Transport
Wenlin Wang, Hongteng Xu, Guoyin Wang +2
We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxili…
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang +4
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental…
Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and wh…
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…
Time-weighted Attentional Session-Aware Recommender System
Mei Wang, Weizhi Li, Yan Yan
Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session a…
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's predi…
Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension
Yuandong Tian
We consider a deep ReLU / Leaky ReLU student network trained from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). The student netwo…
The Differentiable Cross-Entropy Method
Brandon Amos, Denis Yarats
We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us…
Interpreting Knowledge Graph Relation Representation from Word Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be in…
Recurrent Independent Mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann +4
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying caus…
Exploratory Combinatorial Optimization with Reinforcement Learning
Thomas D. Barrett, William R. Clements, Jakob N. Foerster +1
Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With…
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov +1
Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to…
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
Zitao Liu, Zhexuan Xu, Yan Yan
Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building…
PHYRE: A New Benchmark for Physical Reasoning
Anton Bakhtin, Laurens van der Maaten, Justin Johnson +2
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classic…
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma +4
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic cont…
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Stéphane d'Ascoli, Levent Sagun, Joan Bruna +1
Despite the phenomenal success of deep neural networks in a broad range of learning tasks, there is a lack of theory to understand the way they work. In particular, Convolutional N…
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Mahmoud Assran, Joshua Romoff, Nicolas Ballas +2
Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning by stabilizing learning and allowing for higher training throughputs. We propose Gossi…
Beyond Folklore: A Scaling Calculus for the Design and Initialization of ReLU Networks
Aaron Defazio, Léon Bottou
We propose a system for calculating a "scaling constant" for layers and weights of neural networks. We relate this scaling constant to two important quantities that relate to the o…
SVRG for Policy Evaluation with Fewer Gradient Evaluations
Zilun Peng, Ahmed Touati, Pascal Vincent +1
Stochastic variance-reduced gradient (SVRG) is an optimization method originally designed for tackling machine learning problems with a finite sum structure. SVRG was later shown t…
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Anton Bakhtin, Sam Gross, Myle Ott +3
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. W…
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
Diane Bouchacourt, Ludovic Denoyer
Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and sim…
Adaptive Attention Span in Transformers
Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski +1
We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while ma…