3 citations · 9 across the 8 of their papers we have counts for
8 papers
Linear convergence of forward-backward accelerated algorithms without knowledge of the modulus of strong convexity
Bowen Li, Bin Shi, Ya-xiang Yuan
A significant milestone in modern gradient-based optimization was achieved with the development of Nesterov's accelerated gradient descent (NAG) method. This forward-backward techn…
Quantum variational embedding for ground-state energy problems: sum of squares and cluster selection
Bowen Li, Jianfeng Lu
We introduce a sum-of-squares SDP hierarchy approximating the ground-state energy from below for quantum many-body problems, with a natural quantum embedding interpretation. We est…
Remote Sensing Object Counting with Online Knowledge Learning
Shengqin Jiang, Yuan Gao, Bowen Li +3
Efficient models for remote sensing object counting are urgently required for applications in scenarios with limited computing resources, such as drones or embedded systems. A stra…
Proximal Subgradient Norm Minimization of ISTA and FISTA
Bowen Li, Bin Shi, Ya-xiang Yuan
For first-order smooth optimization, the research on the acceleration phenomenon has a long-time history. Until recently, the mechanism leading to acceleration was not successfully…
Vector-wise Joint Diagonalization of Almost Commuting Matrices
Bowen Li, Jianfeng Lu, Ziang Yu
This work aims to numerically construct exactly commuting matrices close to given almost commuting ones, which is equivalent to the joint approximate diagonalization problem. We fi…
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads
Bowen Li, Taeuk Kim, Reinald Kim Amplayo +1
Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the…
An Imitation Learning Approach to Unsupervised Parsing
Bowen Li, Lili Mou, Frank Keller
Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the l…
Dependency Grammar Induction with a Neural Variational Transition-based Parser
Bowen Li, Jianpeng Cheng, Yang Liu +1
Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-ar…