11 citations · 23 across the 15 of their papers we have counts for
7 papers · 1 filter
From Imitation to Discrimination: Progressive Curriculum Learning for Robust Web Navigation
Chuang Peng, Wei Zhang, Renshuai Tao +2
Text-based web agents offer computational efficiency for autonomous web navigation, yet developing robust agents remains challenging due to the noisy and heterogeneous nature of re…
Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference
Yifu Ding, Xinhao Zhang, Jinyang Guo
Transformer-based large language models (LLMs) have demonstrated remarkable performance across a wide range of real-world tasks, but their inference cost remains prohibitively high…
LEKA:LLM-Enhanced Knowledge Augmentation
Xinhao Zhang, Jinghan Zhang, Fengran Mo +3
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's per…
Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation
Xinhao Zhang, Jinghan Zhang, Wujun Si +1
Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks. However, its efficacy is often limited by static training modes and…
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
Jinghan Zhang, Henry Xie, Xinhao Zhang +1
In the financial field, precise risk assessment tools are essential for decision-making. Recent studies have challenged the notion that traditional network loss functions like Mean…
Dynamic and Adaptive Feature Generation with LLM
Xinhao Zhang, Jinghan Zhang, Banafsheh Rekabdar +3
The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algor…
TFWT: Tabular Feature Weighting with Transformer
Xinhao Zhang, Zaitian Wang, Lu Jiang +3
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume…