Search Results for author:
Found 18 papers, 11 papers with code
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Based on this, we propose PruMerge, a novel adaptive visual token reduction approach, which largely reduces the number of visual tokens while maintaining comparable model performance.
FBPT: A Fully Binary Point Transformer
This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model which has the potential to be widely applied and expanded in the fields of robotics and mobile devices.
Online Multi-spectral Neuron Tracing
In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required.
LLM Inference Unveiled: Survey and Roofline Model Insights
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques.
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Diffusion models have achieved remarkable success in image generation tasks, yet their practical deployment is restrained by the high memory and time consumption.
MIM4DD: Mutual Information Maximization for Dataset Distillation
Thus, we introduce mutual information (MI) as the metric to quantify the shared information between the synthetic and the real datasets, and devise MIM4DD numerically maximizing the MI via a newly designed optimizable objective within a contrastive learning framework to update the synthetic dataset.
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
This paper explores a new post-hoc training-free compression paradigm for compressing Large Language Models (LLMs) to facilitate their wider adoption in various computing environments.
PB-LLM: Partially Binarized Large Language Models
This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression.
Causal-DFQ: Causality Guided Data-free Network Quantization
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
BPT: Binary Point Cloud Transformer for Place Recognition
To solve this issue, we propose a binary point cloud transformer for place recognition.
Post-training Quantization on Diffusion Models
These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise.
Lipschitz Continuity Retained Binary Neural Network
Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their corresponding binary vectors, existing works of network binarization frequently adopt the idea of model robustness to reach the aforementioned objective.
Network Binarization via Contrastive Learning
Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.
Supplementing Missing Visions via Dialog for Scene Graph Generations
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks.
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning
Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate the superiority of our novel Fourier analysis based MBP compared to other traditional MBP algorithms.
Contrastive Mutual Information Maximization for Binary Neural Networks
Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.
Lipschitz Continuity Guided Knowledge Distillation
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones.
