4 citations · 10 across the 6 of their papers we have counts for
6 papers
Open-world Multi-label Text Classification with Extremely Weak Supervision
Xintong Li, Jinya Jiang, Ria Dharmani +3
We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without a…
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Jiabao Ji, Bairu Hou, Zhen Zhang +7
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerab…
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
Matteo Farina, Massimiliano Mancini, Elia Cunegatti +3
While excellent in transfer learning, Vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing para…
Training-Free Semantic Segmentation via LLM-Supervision
Wenfang Sun, Yingjun Du, Gaowen Liu +2
Recent advancements in open vocabulary models, like CLIP, have notably advanced zero-shot classification and segmentation by utilizing natural language for class-specific embedding…
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen +6
Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) h…
Causal-DFQ: Causality Guided Data-free Network Quantization
Yuzhang Shang, Bingxin Xu, Gaowen Liu +2
Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devi…