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Found 7 papers, 1 papers with code
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3. 08% and improve 23. 08% of queries' results over the CAT benchmark.
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses.
Heterogeneous Graph Neural Networks for Software Effort Estimation
For time performance, we achieve about 570 seconds as the time performance in both three processes: node embedding initialization, model construction, and story point estimation.
HybridNets: End-to-End Perception Network
Based on these optimizations, we have developed an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously, called HybridNets, which achieves better accuracy than prior art.
Ranked #2 on
Lane Detection
on BDD100K val
Story Point Effort Estimation by Text Level Graph Neural Network
In this paper, we show the potential and possible challenges of Graph Neural Network text classification in story point level estimation.
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations
Automatically generating code from a textual description of method invocation confronts challenges.
