Search Results for author:
Found 11 papers, 2 papers with code
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
In contrast, GQSA integrates quantization and sparsification in a tightly coupled manner, leveraging GPU-friendly structured group sparsity and quantization for efficient acceleration.
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Based on W2*A8 quantization configuration on LLaMA-7B model, it achieved a WikiText2 perplexity of 7. 59 (2. 17$\downarrow $ vs 9. 76 in AffineQuant).
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Based on this observation, we propose an efficient model volume compression strategy, termed FoldGPT, which combines block removal and block parameter sharing. This strategy consists of three parts: (1) Based on the learnable gating parameters, we determine the block importance ranking while modeling the coupling effect between blocks.
A physics-constrained machine learning method for mapping gapless land surface temperature
Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model.
Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection
Considering the inaccurate depth map issue, we collect the RGB features of early stages into a skip convolution module to give more guidance from RGB modality to the final saliency prediction.
Learning Transformer Features for Image Quality Assessment
In addition, comparable NR performance is achieved in extensive experiments, and the results show that the NR performance can be leveraged by the joint training scheme.
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder
Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric for image captioning, which we call Intrinsic Image Captioning Evaluation($I^2CE$).
A Comprehensive Survey of Incentive Mechanism for Federated Learning
Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning.
Intrinsic Image Captioning Evaluation
The image captioning task is about to generate suitable descriptions from images.
An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine
In this paper, we proposed a new method to combine Google Earth Engine (GEE) with multiscale convolutional neural network (MSCNN) to extract urban water from Landsat images, which is summarized as offline training and online prediction (OTOP).
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i. e., the data usability is greatly reduced.
