15 citations · 100 across the 85 of their papers we have counts for
13 papers · 1 filter
Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation
Rongyu Zhang, Yulin Luo, Jiaming Liu +8
The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adve…
StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
Akio Kodaira, Chenfeng Xu, Toshiki Hazama +8
We introduce StreamDiffusion, a real-time diffusion pipeline designed for interactive image generation. Existing diffusion models are adept at creating images from text or image pr…
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Anthony Chen, Huanrui Yang, Yulu Gan +7
Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce…
An LLM Compiler for Parallel Function Calling
Sehoon Kim, Suhong Moon, Ryan Tabrizi +4
The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skil…
Simple and Effective Input Reformulations for Translation
Brian Yu, Hansen Lillemark, Kurt Keutzer
Foundation language models learn from their finetuning input context in different ways. In this paper, we reformulate inputs during finetuning for challenging translation tasks, le…
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Lin Xu, Zhiyuan Hu, Daquan Zhou +5
Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities. As their application…
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome
Chenyu Wang, Zhen Dong, Daquan Zhou +4
The utilization of large-scale neural networks on Processing-In-Memory (PIM) accelerators encounters challenges due to constrained on-chip memory capacity. To tackle this issue, cu…
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Ying Sheng, Shiyi Cao, Dacheng Li +9
The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often…
CVPR 2023 Text Guided Video Editing Competition
Jay Zhangjie Wu, Xiuyu Li, Difei Gao +17
Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing…
Large Language Models are Visual Reasoning Coordinators
Liangyu Chen, Bo Li, Sheng Shen +5
Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsen…
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Zhikai Li, Xiaoxuan Liu, Banghua Zhu +3
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning these pretrained models on downstream datase…
HallE-Control: Controlling Object Hallucination in Large Multimodal Models
Bohan Zhai, Shijia Yang, Chenfeng Xu +4
Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that i…
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Xiuying Wei, Yunchen Zhang, Yuhang Li +4
Post-training quantization~(PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outl…