11 citations · 53 across the 14 of their papers we have counts for
14 papers
Cross-Modal Memory Compression for Efficient Multi-Agent Debate
Jing Wu, Yue Sun, Tianpei Xie +7
Multi-agent debate can improve reasoning quality and reduce hallucinations, but it incurs rapidly growing context as debate rounds and agent count increase. Retaining full textual…
Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL
Renat Sergazinov, Jing Wu, Shao-An Yin
While random Fourier features are a classic tool in kernel methods, their utility as a pre-processing step for deep learning on tabular data has been largely overlooked. Motivated…
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
Suiyao Chen, Jing Wu, Yunxiao Wang +6
Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive le…
CROPS: A Deployable Crop Management System Over All Possible State Availabilities
Jing Wu, Zhixin Lai, Shengjie Liu +6
Exploring the optimal management strategy for nitrogen and irrigation has a significant impact on crop yield, economic profit, and the environment. To tackle this optimization chal…
The New Agronomists: Language Models are Experts in Crop Management
Jing Wu, Zhixin Lai, Suiyao Chen +3
Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimiz…
Residual-based Language Models are Free Boosters for Biomedical Imaging
Zhixin Lai, Jing Wu, Suiyao Chen +2
In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of…
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Jing Wu, Suiyao Chen, Qi Zhao +9
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or…
ReConTab: Regularized Contrastive Representation Learning for Tabular Data
Suiyao Chen, Jing Wu, Naira Hovakimyan +1
Representation learning stands as one of the critical machine learning techniques across various domains. Through the acquisition of high-quality features, pre-trained embeddings s…
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing
Jing Wu, Naira Hovakimyan, Jennifer Hobbs
Classifying and segmenting patterns from a limited number of examples is a significant challenge in remote sensing and earth observation due to the difficulty in acquiring accurate…
Hallucination Improves the Performance of Unsupervised Visual Representation Learning
Jing Wu, Jennifer Hobbs, Naira Hovakimyan
Contrastive learning models based on Siamese structure have demonstrated remarkable performance in self-supervised learning. Such a success of contrastive learning relies on two co…
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
Jing Wu, David Pichler, Daniel Marley +3
A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is…
Balanced Training for Sparse GANs
Yite Wang, Jing Wu, Naira Hovakimyan +1
Over the past few years, there has been growing interest in developing larger and deeper neural networks, including deep generative models like generative adversarial networks (GAN…
Optimizing Crop Management with Reinforcement Learning and Imitation Learning
Ran Tao, Pan Zhao, Jing Wu +5
Crop management, including nitrogen (N) fertilization and irrigation management, has a significant impact on the crop yield, economic profit, and the environment. Although manageme…
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations
Jing Wu, Ran Tao, Pan Zhao +2
Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper pro…