9 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
Optimization-Inspired Few-Shot Adaptation for Large Language Models
Boyan Gao, Xin Wang, Yibo Yang +1
Large Language Models (LLMs) have demonstrated remarkable performance in real-world applications. However, adapting LLMs to novel tasks via fine-tuning often requires substantial t…
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?
Shreyank N Gowda, Boyan Gao, Xiao Gu +1
Video understanding has shown remarkable improvements in recent years, largely dependent on the availability of large scaled labeled datasets. Recent advancements in visual-languag…
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…
Exploring the latent space of diffusion models directly through singular value decomposition
Li Wang, Boyan Gao, Yanran Li +4
Despite the groundbreaking success of diffusion models in generating high-fidelity images, their latent space remains relatively under-explored, even though it holds significant pr…
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Boyan Gao, Bo Zhao, Shreyank N Gowda +4
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produce…