12 citations · 37 across the 8 of their papers we have counts for
8 papers
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Yijun Liu, Yuan Meng, Fang Wu +7
Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world application…
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search
Siao Tang, Xin Wang, Hong Chen +3
Diffusion models have recently shown remarkable generation ability, achieving state-of-the-art performance in many tasks. However, the high computational cost is still a troubling…
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing
Siao Tang, Xin Wang, Hong Chen +4
High computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, mos…
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz +6
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…
AutoGL: A Library for Automated Graph Learning
Ziwei Zhang, Yijian Qin, Zeyang Zhang +11
Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for…
MetaDelta: A Meta-Learning System for Few-shot Image Classification
Yudong Chen, Chaoyu Guan, Zhikun Wei +2
Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks. Naturally, few-shot learning has been one of…
Semantic Role Labeling with Associated Memory Network
Chaoyu Guan, Yuhao Cheng, Hai Zhao
Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest…