23 citations · 102 across the 34 of their papers we have counts for
12 papers · 1 filter
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
Le Chen, Arijit Bhattacharjee, Nesreen K. Ahmed +4
Large language models (LLMs) have become increasingly prominent in academia and industry due to their remarkable performance in diverse applications. As these models evolve with in…
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated learning (FL) offers privacy-preserving decentralized machine learning, optimizing models at edge clients without sharing private data. Simultaneously, foundation models…
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Le Chen, Quazi Ishtiaque Mahmud, Hung Phan +2
Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis an…
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amo…
Accelerating Domain-aware Deep Learning Models with Distributed Training
Aishwarya Sarkar, Chaoqun Lu, Ali Jannesari
Recent advances in data-generating techniques led to an explosive growth of geo-spatiotemporal data. In domains such as hydrology, ecology, and transportation, interpreting the com…
Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processing
Waqwoya Abebe, Ali Jannesari
Recently, local peer topology has been shown to influence the overall convergence of decentralized learning (DL) graphs in the presence of data heterogeneity. In this paper, we dem…
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Aishwarya Sarkar, Jien Zhang, Chaoqun Lu +1
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidd…
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…
Temporal Shift Reinforcement Learning
Deepak George Thomas, Tichakorn Wongpiromsarn, Ali Jannesari
The function approximators employed by traditional image-based Deep Reinforcement Learning (DRL) algorithms usually lack a temporal learning component and instead focus on learning…
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Sixing Yu, Phuong Nguyen, Ali Anwar +1
Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge dev…
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis
Aishwarya Sarkar, Jien Zhang, Chaoqun Lu +1
Due to limited evidence and complex causes of regional climate change, the confidence in predicting fluvial floods remains low. Understanding the fundamental mechanisms intrinsic t…
Static Neural Compiler Optimization via Deep Reinforcement Learning
Rahim Mammadli, Ali Jannesari, Felix Wolf
The phase-ordering problem of modern compilers has received a lot of attention from the research community over the years, yet remains largely unsolved. Various optimization sequen…