Evanston
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Neural Information Processing Systems http://nips.cc/
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.28)
- Asia > China > Hong Kong (0.04)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- (3 more...)
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu
Without careful design, GCNs can fail miserably even with multiple layers and nonlinear activation functions.
- North America > United States > Massachusetts > Suffolk County > Boston (0.05)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Europe > Hungary > Budapest > Budapest (0.04)
Efficient Graph Matching for Correlated Stochastic Block Models
We study learning problems on correlated stochastic block models with two balanced communities.
- North America > United States > Illinois > Cook County > Evanston (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > Middle East > Israel (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.14)
- South America > Chile > Santiago Metropolitan Region > Santiago Province > Santiago (0.04)
- Asia > China > Beijing > Beijing (0.04)
- (3 more...)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- North America > United States > Indiana > Tippecanoe County > West Lafayette (0.04)
- North America > United States > Indiana > Tippecanoe County > Lafayette (0.04)
- (2 more...)
- Leisure & Entertainment > Sports (1.00)
- Leisure & Entertainment > Games > Computer Games (0.94)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.93)
- (3 more...)
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes Jerry Y ao-Chieh Hu Dennis Wu
We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories.
- South America > Brazil (0.04)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- Europe > Austria > Vienna (0.04)
- Asia > Middle East > Jordan (0.04)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)
- (4 more...)
Convergent Policy Optimization for Safe Reinforcement Learning
Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang
Given ,J ( )andD ( )arethesample (i.e., atrajectory) . Note J ( ) and D ( ) are randomness J ( )andD ( )todenote anda ClearlyweJ( )= E J ( ) andD( )= E D ( ) .
- North America > United States > Illinois > Cook County > Chicago (0.05)
- South America > Chile > Santiago Metropolitan Region > Santiago Province > Santiago (0.04)
- North America > United States > Pennsylvania (0.04)
- (5 more...)
Provable Gaussian Embedding with One Observation
Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang
Neural Information Processing Systems http://nips.cc/
- North America > United States > Illinois > Cook County > Chicago (0.05)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- North America > United States > Illinois > Cook County > Evanston (0.04)
- (2 more...)
Embedding Dimension of Contrastive Learning and k-Nearest Neighbors
We study the embedding dimension of distance comparison data in two settings: contrastive learning and k -nearest neighbors ( k -NN).
- North America > Canada > Ontario > Toronto (0.14)
- Europe > Austria > Vienna (0.14)
- North America > United States > Maryland > Baltimore (0.04)
- (19 more...)