Search Results for author:
Found 8 papers, 1 papers with code
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the whole LLMs.
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models.
Mathematical Foundation and Corrections for Full Range Head Pose Estimation
This paper presents (1) code and algorithms for inferring coordinate system from provided source code, code for Euler angle application order and extracting precise rotation matrices and the Euler angles, (2) code and algorithms for converting poses from one rotation system to another, (3) novel formulae for 2D augmentations of the rotation matrices, and (4) derivations and code for the correct drawing routines for rotation matrices and poses.
Asynchronous Distributed Optimization with Delay-free Parameters
In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays.
Delay-adaptive step-sizes for asynchronous learning
In this paper, we show that it is possible to use learning rates that depend on the actual time-varying delays in the system.
A Multi-task Learning Framework for Product Ranking with BERT
The proposed model utilizes domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs multi-task learning to optimize multiple objectives simultaneously, which yields a general end-to-end learning framework for product search.
Distributed Optimization with Coupling Constraints
In this paper, we develop a novel distributed algorithm for addressing convex optimization with both nonlinear inequality and linear equality constraints, where the objective function can be a general nonsmooth convex function and all the constraints can be fully coupled.
Distributed Optimization
Optimization and Control
