18 citations · 62 across the 15 of their papers we have counts for
15 papers
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
Yihua Zhang, Pingzhi Li, Junyuan Hong +10
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become s…
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal +4
For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding care…
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
Wenqing Zheng, Edward W Huang, Nikhil Rao +2
Link prediction is central to many real-world applications, but its performance may be hampered when the graph of interest is sparse. To alleviate issues caused by sparsity, we inv…
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search
Wenqing Zheng, S P Sharan, Zhiwen Fan +3
Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural ne…
Search Behavior Prediction: A Hypergraph Perspective
Yan Han, Edward W Huang, Wenqing Zheng +3
Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence h…
Symbolic Distillation for Learned TCP Congestion Control
S P Sharan, Wenqing Zheng, Kuo-Feng Hsu +3
Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to lear…
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang +5
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla…
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice
Peihao Wang, Wenqing Zheng, Tianlong Chen +1
Vision Transformer (ViT) has recently demonstrated promise in computer vision problems. However, unlike Convolutional Neural Networks (CNN), it is known that the performance of ViT…
Symbolic Learning to Optimize: Towards Interpretability and Scalability
Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu +1
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameter…
Web of Scholars: A Scholar Knowledge Graph
Jiaying Liu, Jing Ren, Wenqing Zheng +3
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scho…
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Wenqing Zheng, Edward W Huang, Nikhil Rao +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification, regression, and recommendation tasks. GNNs work well when rich and high-quality conn…
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
Wenqing Zheng, Qiangqiang Guo, Hao Yang +2
Multi-agent control is a central theme in the Cyber-Physical Systems (CPS). However, current control methods either receive non-Markovian states due to insufficient sensing and dec…
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
Tianlong Chen, Kaixiong Zhou, Keyu Duan +4
Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uni…
Structured DropConnect for Uncertainty Inference in Image Classification
Wenqing Zheng, Jiyang Xie, Weidong Liu +1
With the complexity of the network structure, uncertainty inference has become an important task to improve the classification accuracy for artificial intelligence systems. For ima…
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks
Ting-Kuei Hu, Fernando Gama, Tianlong Chen +4
In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-contro…