research
∙
08/09/2023
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Neural Machine Translation (NMT) is widely applied in software engineeri...
share
research
∙
05/31/2023
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
The remarkable growth and significant success of machine learning have e...
research
∙
05/19/2023
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Foundation Models (FMs), such as BERT, GPT, ViT, and CLIP, have demonstr...
research
∙
05/09/2023
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Detecting parallelizable code regions is a challenging task, even for ex...
research
∙
05/01/2023
Interpreting Pretrained Source-code Models using Neuron Redundancy Analyses
Neural code intelligence models continue to be 'black boxes' to the huma...
research
∙
04/25/2023
Performance Optimization using Multimodal Modeling and Heterogeneous GNN
Growing heterogeneity and configurability in HPC architectures has made ...
research
∙
04/10/2023
GraphBinMatch: Graph-based Similarity Learning for Cross-Language Binary and Source Code Matching
Matching binary to source code and vice versa has various applications i...
research
∙
02/22/2023
Power Constrained Autotuning using Graph Neural Networks
Recent advances in multi and many-core processors have led to significan...
research
∙
01/25/2023
Accelerating Domain-aware Deep Learning Models with Distributed Training
Recent advances in data-generating techniques led to an explosive growth...
research
∙
12/16/2022
Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processing
Recently, local peer topology has been shown to influence the overall co...
research
∙
12/13/2022
Towards Seamless Management of AI Models in High-Performance Computing
With the increasing prevalence of artificial intelligence (AI) in divers...
research
∙
11/09/2022
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
Federated Learning (FL) is extensively used to train AI/ML models in dis...
research
∙
08/16/2022
Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion
With increasing concern about user data privacy, federated learning (FL)...
research
∙
06/22/2022
Heterogeneous Graph Neural Networks for Software Effort Estimation
Software effort can be measured by story point [35]. Current approaches ...
research
∙
03/06/2022
Story Point Effort Estimation by Text Level Graph Neural Network
Estimating the software projects' efforts developed by agile methods is ...
research
∙
03/01/2022
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization
There is a large space of NUMA and hardware prefetcher configurations th...
research
∙
12/01/2021
CLAWS: Contrastive Learning with hard Attention and Weak Supervision
Learning effective visual representations without human supervision is a...
research
∙
11/29/2021
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Efficient federated learning is one of the key challenges for training a...
research
∙
10/02/2021
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Extracting and meticulously analyzing geo-spatiotemporal features is cru...
research
∙
06/13/2021
Adaptive Dynamic Pruning for Non-IID Federated Learning
Federated Learning (FL) has emerged as a new paradigm of training machin...
research
∙
05/25/2021
Interpretable UAV Collision Avoidance using Deep Reinforcement Learning
The significant components of any successful autonomous flight system ar...
research
∙
03/11/2021
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance
Integration of reinforcement learning with unmanned aerial vehicles (UAV...
research
∙
02/05/2021
GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
Model compression is an essential technique for deploying deep neural ne...
research
∙
11/25/2020
Auto Graph Encoder-Decoder for Model Compression and Network Acceleration
Model compression aims to deploy deep neural networks (DNN) to mobile de...
research
∙
10/09/2020
HydroDeep – A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis
Floods are one of the major climate-related disasters, leading to substa...
research
∙
08/20/2020
Static Neural Compiler Optimization via Deep Reinforcement Learning
The phase-ordering problem of modern compilers has received a lot of att...
research
∙
07/15/2019
DeepRace: Finding Data Race Bugs via Deep Learning
With the proliferation of multi-core hardware, parallel programs have be...
research
∙
07/14/2019
Automatic Repair and Type Binding of Undeclared Variables using Neural Networks
Deep learning had been used in program analysis for the prediction of hi...
research
∙
05/30/2019
Efficient Object Detection Model for Real-Time UAV Applications
Unmanned Aerial Vehicles (UAVs) especially drones, equipped with vision ...
research
∙
11/21/2016