2 citations · 7 across the 8 of their papers we have counts for
8 papers
Robust Score-Based Quickest Change Detection
Sean Moushegian, Suya Wu, Enmao Diao +3
Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able…
ColA: Collaborative Adaptation with Gradient Learning
Enmao Diao, Qi Le, Suya Wu +4
A primary function of back-propagation is to compute both the gradient of hidden representations and parameters for optimization with gradient descent. Training large models requir…
Robust Quickest Change Detection for Unnormalized Models
Suya Wu, Enmao Diao, Taposh Banerjee +2
Detecting an abrupt and persistent change in the underlying distribution of online data streams is an important problem in many applications. This paper proposes a new robust score…
Quickest Change Detection for Unnormalized Statistical Models
Suya Wu, Enmao Diao, Taposh Banerjee +2
Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models b…
Minimax Concave Penalty Regularized Adaptive System Identification
Bowen Li, Suya Wu, Erin E. Tripp +2
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communicat…
Multi-Agent Adversarial Attacks for Multi-Channel Communications
Juncheng Dong, Suya Wu, Mohammadreza Sultani +1
Recently Reinforcement Learning (RL) has been applied as an anti-adversarial remedy in wireless communication networks. However, studying the RL-based approaches from the adversary…
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections
Mohammadreza Soltani, Suya Wu, Yuerong Li +2
We propose a new structured pruning framework for compressing Deep Neural Networks (DNNs) with skip connections, based on measuring the statistical dependency of hidden layers and…
Deep Clustering of Compressed Variational Embeddings
Suya Wu, Enmao Diao, Jie Ding +1
Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bern…