24 citations · 58 across the 50 of their papers we have counts for
16 papers · 1 filter
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation
Le Chen, Nuo Xu, Winson Chen +7
Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emer…
PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu +2
Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequen…
OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
Arijit Bhattacharjee, Ali TehraniJamsaz, Le Chen +4
Recent advances in large language models (LLMs) have significantly accelerated progress in code translation, enabling more accurate and efficient transformation across programming…
Analyzing Latent Concepts in Code Language Models
Arushi Sharma, Vedant Pungliya, Christopher J. Quinn +1
Interpreting the internal behavior of large language models trained on code remains a critical challenge, particularly for applications demanding trust, transparency, and semantic…
VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +8
Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Lan…
Learning Bug Context for PyTorch-to-JAX Translation with LLMs
Hung Phan, Son Vu, Tuan Dinh +3
Large language models (LLMs) have shown strong performance on code translation between widely used programming languages. However, translation becomes much less reliable for domain…
ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization
Sadegh Jafari, Aishwarya Sarkar, Mohiuddin Bilwal +1
Foundation models face growing compute and memory bottlenecks, hindering deployment on resource-limited platforms. While compression techniques such as pruning and quantization are…
HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
Aishwarya Sarkar, Autrin Hakimi, Xiaoqiong Chen +4
Accurate flood forecasting remains a challenge for water-resource management, as it demands modeling of local, time-varying runoff drivers (e.g., rainfall-induced peaks, baseflow t…
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation
Fatema Siddika, Md Anwar Hossen, J. Pablo Muñoz +3
Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an…
Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypes
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang +3
Heterogeneous Federated Learning (HFL) has gained significant attention for its capacity to handle both model and data heterogeneity across clients. Prototype-based HFL methods eme…
Generalizing Scaling Laws for Dense and Sparse Large Language Models
Md Arafat Hossain, Xingfu Wu, Valerie Taylor +1
Despite recent advancements of large language models (LLMs), optimally predicting the model size for LLM pretraining or allocating optimal resources still remains a challenge. Seve…
Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning
Ankit Jyothish, Ali Jannesari
Graph transformers typically embed every node in a single Euclidean space, blurring heterogeneous topologies. We prepend a lightweight Riemannian mixture-of-experts layer that rout…
Enhanced Soups for Graph Neural Networks
Joseph Zuber, Aishwarya Sarkar, Joseph Jennings +1
Graph Neural Networks (GNN) have demonstrated state-of-the-art performance in numerous scientific and high-performance computing (HPC) applications. Recent work suggests that "soup…
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency
Duy Phuong Nguyen, J. Pablo Munoz, Tanya Roosta +1
Federated Learning (FL) enables collaborative learning across distributed clients while preserving data privacy. However, FL faces significant challenges when dealing with heteroge…
Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration
Le Chen, Bin Lei, Dunzhi Zhou +4
Translating legacy Fortran code into C++ is a crucial step in modernizing high-performance computing (HPC) applications. However, the scarcity of high-quality, parallel Fortran-to-…
SuperSAM: Crafting a SAM Supernetwork via Structured Pruning and Unstructured Parameter Prioritization
Waqwoya Abebe, Sadegh Jafari, Sixing Yu +6
Neural Architecture Search (NAS) is a powerful approach of automating the design of efficient neural architectures. In contrast to traditional NAS methods, recently proposed one-sh…