Search Results for author: Bin Lei

Found 11 papers, 4 papers with code

FlashVideo: A Framework for Swift Inference in Text-to-Video Generation

no code implementations30 Dec 2023 Bin Lei, Le Chen, Caiwen Ding

In the evolving field of machine learning, video generation has witnessed significant advancements with autoregressive-based transformer models and diffusion models, known for synthesizing dynamic and realistic scenes.

Text-to-Video Generation Video Generation

Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis

no code implementations2 Dec 2023 Kiran Thorat, Jiahui Zhao, Yaotian Liu, Hongwu Peng, Xi Xie, Bin Lei, Jeff Zhang, Caiwen Ding

The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instructions, forms the core of this investigation.

Language Modelling Large Language Model

CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset

no code implementations11 Nov 2023 Le Chen, Arijit Bhattacharjee, Nesreen K. Ahmed, Niranjan Hasabnis, Gal Oren, Bin Lei, Ali Jannesari

The evaluation of CompCodeVet on two open-source code datasets shows that CompCodeVet has the ability to improve the training dataset quality for LLMs.

C++ code Code Generation +2

Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought

no code implementations16 Aug 2023 Bin Lei, Pei-Hung Lin, Chunhua Liao, Caiwen Ding

Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries.

Logical Reasoning

Towards Zero Memory Footprint Spiking Neural Network Training

no code implementations16 Aug 2023 Bin Lei, Sheng Lin, Pei-Hung Lin, Chunhua Liao, Caiwen Ding

Our design is able to achieve a $\mathbf{58. 65\times}$ reduction in memory usage compared to the current SNN node.

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++

1 code implementation15 Jul 2023 Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code.

C++ code Code Translation +1

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

no code implementations24 Apr 2023 Shaoyi Huang, Haowen Fang, Kaleel Mahmood, Bowen Lei, Nuo Xu, Bin Lei, Yue Sun, Dongkuan Xu, Wujie Wen, Caiwen Ding

Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).

Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach

no code implementations6 Nov 2022 Bin Lei, Shaoyi Huang, Caiwen Ding, Monika Filipovska

We consider the problem of long-term traffic speed forecasting for a real large-scale transportation network data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS).

Decision Making Graph Attention +2

Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

1 code implementation6 Jan 2020 Zhongling Huang, Corneliu Octavian Dumitru, Zongxu Pan, Bin Lei, Mihai Datcu

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging.

General Classification Transfer Learning +1

What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs

1 code implementation4 Jun 2019 Zhongling Huang, Zongxu Pan, Bin Lei

Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets.

Domain Adaptation Transfer Learning

Learning a Rotation Invariant Detector with Rotatable Bounding Box

4 code implementations26 Nov 2017 Lei Liu, Zongxu Pan, Bin Lei

The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects.

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