297 citations · 958 across the 50 of their papers we have counts for
14 papers · 1 filter
CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs
Zhaojing Zhou, Xunchao Li, Minghao Li +8
The rapid scaling of Large Language Models (LLMs) elevates inference costs and compounds substantial deployment barriers. While quantization to 8 or 4 bits mitigates this, sub-3-bi…
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu, Shaogao Lv, Dun Zeng +3
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their pri…
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han +6
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the un…
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
Youlong Ding, Xueyang Wu, Hao Wang +1
The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer mode…
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
Renzhe Xu, Haotian Wang, Xingxuan Zhang +2
Competitions for shareable and limited resources have long been studied with strategic agents. In reality, agents often have to learn and maximize the rewards of the resources at t…
Cross-Frequency Time Series Meta-Forecasting
Mike Van Ness, Huibin Shen, Hao Wang +3
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time serie…
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy +6
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in…
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Haorui Wang, Haoteng Yin, Muhan Zhang +1
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif…
Delving into Deep Imbalanced Regression
Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen +2
Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focu…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…
Neural Network Design: Learning from Neural Architecture Search
Bas van Stein, Hao Wang, Thomas Bäck
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interes…
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Hengguan Huang, Fuzhao Xue, Hao Wang +1
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory s…
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Zhongkai Hao, Chengqiang Lu, Zheyuan Hu +5
Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of sc…
Secret Sharing based Secure Regressions with Applications
Chaochao Chen, Liang Li, Wenjing Fang +6
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potent…