665 citations · 899 across the 15 of their papers we have counts for
11 papers · 1 filter
Contrastive Laplacian Eigenmaps
Hao Zhu, Ke Sun, Piotr Koniusz
Graph contrastive learning attracts/disperses node representations for similar/dissimilar node pairs under some notion of similarity. It may be combined with a low-dimensional embe…
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Ke Sun, Yafei Wang, Yi Liu +5
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…
Graph Learning: A Survey
Feng Xia, Ke Sun, Shuo Yu +4
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social sys…
Graph Force Learning
Ke Sun, Jiaying Liu, Shuo Yu +2
Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, incre…
Effectiveness of MPC-friendly Softmax Replacement
Marcel Keller, Ke Sun
Softmax is widely used in deep learning to map some representation to a probability distribution. As it is based on exp/log functions that are relatively expensive in multi-party c…
Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions
Ke Sun, Frank Nielsen
This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information. We relat…