activity
20192026
most citedBridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

68 citations · 135 across the 38 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

cs.CL2023★ 5 cited

G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

Jiahui Gao, Renjie Pi, Jipeng Zhang +8

Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mat…

cs.CV2023★ 1 cited

PerceptionGPT: Effectively Fusing Visual Perception into LLM

Renjie Pi, Lewei Yao, Jiahui Gao +2

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities, giving rise to visual large language models (VLLM…

cs.LG2023★ 10 cited

Mitigating the Alignment Tax of RLHF

Yong Lin, Hangyu Lin, Wei Xiong +14

LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…

cs.CV2023

DetGPT: Detect What You Need via Reasoning

Renjie Pi, Jiahui Gao, Shizhe Diao +8

In recent years, the field of computer vision has seen significant advancements thanks to the development of large language models (LLMs). These models have enabled more effective…

cs.LG2023

Effective Bilevel Optimization via Minimax Reformulation

Xiaoyu Wang, Rui Pan, Renjie Pi +1

Bilevel optimization has found successful applications in various machine learning problems, including hyper-parameter optimization, data cleaning, and meta-learning. However, its…

cs.LG2023

Probabilistic Bilevel Coreset Selection

Xiao Zhou, Renjie Pi, Weizhong Zhang +2

The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the en…

cs.LG2023★ 10 cited

Model Agnostic Sample Reweighting for Out-of-Distribution Learning

Xiao Zhou, Yong Lin, Renjie Pi +4

Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of…