Poster "model compression" Papers
104 papers found • Page 2 of 3
Conference
The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
Tian Jin, Ahmed Imtiaz Humayun, Utku Evci et al.
The Unreasonable Ineffectiveness of the Deeper Layers
Andrey Gromov, Kushal Tirumala, Hassan Shapourian et al.
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks
Xiang Meng, Mehdi Makni, Rahul Mazumder
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
Yoojin Jung, Byung Cheol Song
Variance-Based Pruning for Accelerating and Compressing Trained Networks
Uranik Berisha, Jens Mehnert, Alexandru Condurache
VLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving
Ruifei Zhang, Wei Zhang, Xiao Tan et al.
What Makes a Good Dataset for Knowledge Distillation?
Logan Frank, Jim Davis
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
Yeonhong Park, Jake Hyun, SangLyul Cho et al.
Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
Luyang Fang, Yongkai Chen, Wenxuan Zhong et al.
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin et al.
Binarized Low-light Raw Video Enhancement
Gengchen Zhang, Yulun Zhang, Xin Yuan et al.
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
Bo-Kyeong Kim, Hyoung-Kyu Song, Thibault Castells et al.
CHAI: Clustered Head Attention for Efficient LLM Inference
Saurabh Agarwal, Bilge Acun, Basil Hosmer et al.
CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
K L Navaneet, Kossar Pourahmadi, Soroush Abbasi Koohpayegani et al.
Compressing Large Language Models by Joint Sparsification and Quantization
Jinyang Guo, Jianyu Wu, Zining Wang et al.
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Junyuan Hong, Jinhao Duan, Chenhui Zhang et al.
DeepCache: Accelerating Diffusion Models for Free
Xinyin Ma, Gongfan Fang, Xinchao Wang
DFD: Distilling the Feature Disparity Differently for Detectors
Kang Liu, Yingyi Zhang, Jingyun Zhang et al.
Distilling Knowledge from Large-Scale Image Models for Object Detection
Gang Li, Wenhai Wang, Xiang Li et al.
DistiLLM: Towards Streamlined Distillation for Large Language Models
Jongwoo Ko, Sungnyun Kim, Tianyi Chen et al.
Do Topological Characteristics Help in Knowledge Distillation?
Jungeun Kim, Junwon You, Dongjin Lee et al.
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal et al.
Efficient Multitask Dense Predictor via Binarization
Yuzhang Shang, Dan Xu, Gaowen Liu et al.
Enhanced Sparsification via Stimulative Training
Shengji Tang, Weihao Lin, Hancheng Ye et al.
Enhancing Vision Transformer: Amplifying Non-Linearity in Feedforward Network Module
Yixing Xu, Chao Li, Dong Li et al.
ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking
Wenshuo Li, Xinghao Chen, Han Shu et al.
Exploring Intrinsic Dimension for Vision-Language Model Pruning
Hanzhang Wang, Jiawen Zhang, Qingyuan Ma
Extreme Compression of Large Language Models via Additive Quantization
Vage Egiazarian, Andrei Panferov, Denis Kuznedelev et al.
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Hong Huang, Weiming Zhuang, Chen Chen et al.
Fixed Point Diffusion Models
Luke Melas-Kyriazi, Xingjian Bai
Flextron: Many-in-One Flexible Large Language Model
Ruisi Cai, Saurav Muralidharan, Greg Heinrich et al.
FrameQuant: Flexible Low-Bit Quantization for Transformers
Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang et al.
Good Teachers Explain: Explanation-Enhanced Knowledge Distillation
Amin Parchami, Moritz Böhle, Sukrut Rao et al.
How Far Can We Compress Instant-NGP-Based NeRF?
Yihang Chen, Qianyi Wu, Mehrtash Harandi et al.
Instance-Aware Group Quantization for Vision Transformers
Jaehyeon Moon, Dohyung Kim, Jun Yong Cheon et al.
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights $\textit{Irreversibly}$ and $\textit{Monotonically}$ Impairs ``Difficult" Downstream Tasks in LLMs
Lu Yin, Ajay Jaiswal, Shiwei Liu et al.
KernelWarehouse: Rethinking the Design of Dynamic Convolution
Chao Li, Anbang Yao
Lightweight Image Super-Resolution via Flexible Meta Pruning
Yulun Zhang, Kai Zhang, Luc Van Gool et al.
Localizing Task Information for Improved Model Merging and Compression
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez et al.
MoEAD: A Parameter-efficient Model for Multi-class Anomaly Detection
Shiyuan Meng, Wenchao Meng, Qihang Zhou et al.
Neural Metamorphosis
Xingyi Yang, Xinchao Wang
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
Kaituo Feng, Changsheng Li, Dongchun Ren et al.
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
Yangyang Guo, Guangzhi Wang, Mohan Kankanhalli
Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models
Peijie Dong, Lujun Li, Zhenheng Tang et al.
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation
Yizhe Xiong, Hui Chen, Tianxiang Hao et al.
Rethinking Optimization and Architecture for Tiny Language Models
Yehui Tang, Kai Han, Fangcheng Liu et al.
Reweighted Solutions for Weighted Low Rank Approximation
David Woodruff, Taisuke Yasuda
SAGS: Structure-Aware 3D Gaussian Splatting
Evangelos Ververas, Rolandos Alexandros Potamias, Song Jifei et al.
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim et al.
SNP: Structured Neuron-level Pruning to Preserve Attention Scores
Kyunghwan Shim, Jaewoong Yun, Shinkook Choi