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Found 17 papers, 8 papers with code
Observer-Based Data-Driven Consensus Control for Nonlinear Multi-Agent Systems against DoS and FDI attacks
Existing data-driven control methods generally do not address False Data Injection (FDI) and Denial-of-Service (DoS) attacks simultaneously.
Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration
Translating legacy Fortran code into C++ is a crucial step in modernizing high-performance computing (HPC) applications.
Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage
Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle to \textbf{autonomously solve the real world engineering problem}.
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system
To tackle these challenges and improve the code generation performance for automated programming systems, we propose Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization (i. e., FALCON).
AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90. 9\%}$ vs. $\mathbf{90. 2\%}$).
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
Firstly, their effectiveness in tackling complex mathematical problems is somewhat constrained.
Ranked #3 on
Math Word Problem Solving
on MATH
FlashVideo: A Framework for Swift Inference in Text-to-Video Generation
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.
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis
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.
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
The evaluation of CompCodeVet on two open-source code datasets shows that CompCodeVet has the ability to improve the training dataset quality for LLMs.
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries.
Towards Zero Memory Footprint Spiking Neural Network Training
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++
In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code.
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
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
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).
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
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.
What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs
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.
Learning a Rotation Invariant Detector with Rotatable Bounding Box
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.
