Search Results for author:
Found 6 papers, 4 papers with code
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
Enhancing Efficiency in Sparse Models with Sparser Selection
To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models.
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
In this paper, we explore how to enable intelligent cost-effective interactions between the agent and an LLM.
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently.
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).
No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence
Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common.
