456 citations · 515 across the 7 of their papers we have counts for
7 papers
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Shaoyi Huang, Bowen Lei, Dongkuan Xu +4
Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popula…
System Identification via Nuclear Norm Regularization
Yue Sun, Samet Oymak, Maryam Fazel
This paper studies the problem of identifying low-order linear systems via Hankel nuclear norm regularization. Hankel regularization encourages the low-rankness of the Hankel matri…
Towards Sample-efficient Overparameterized Meta-learning
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk +2
An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain th…
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak +1
Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from comm…
ResNeSt: Split-Attention Networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang +9
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies t…
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis
Weijun Lu, Xiao-Yang Liu, Qingwei Wu +2
We propose a novel multilinear dynamical system (MLDS) in a transform domain, named -MLDS, to model tensor time series. With transformations applied to a tensor data,…
Low-Rank Positive Semidefinite Matrix Recovery from Corrupted Rank-One Measurements
Yuanxin Li, Yue Sun, Yuejie Chi
We study the problem of estimating a low-rank positive semidefinite (PSD) matrix from a set of rank-one measurements using sensing vectors composed of i.i.d. standard Gaussian entr…