Nadav Schneider

According to our database, Nadav Schneider authored at least 9 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Reactor Optimization Benchmark by Reinforcement Learning.

Deborah Schwarcz, Nadav Schneider, Gal Oren, Uri Steinitz

CoRR, 2024

MPIrigen: MPI Code Generation through Domain-Specific Language Models.

Nadav Schneider, Niranjan Hasabnis, Vy A. Vo, Tal Kadosh, Neva Krien, Mihai Capota, Abdul Wasay, Guy Tamir, Ted Willke, Nesreen K. Ahmed, Yuval Pinter, Timothy G. Mattson, Gal Oren

CoRR, 2024

2023

Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks.

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen K. Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy G. Mattson, Gal Oren

CoRR, 2023

Scope is all you need: Transforming LLMs for HPC Code.

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen K. Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy G. Mattson, Gal Oren

CoRR, 2023

Explainable Multi-View Deep Networks Methodology for Experimental Physics.

Nadav Schneider, Muriel Tzdaka, Galit Sturm, Guy Lazovski, Galit Bar, Gilad Oren, Raz Gvishi, Gal Oren

CoRR, 2023

Hate Speech Targets Detection in Parler using BERT.

Nadav Schneider, Shimon Shouei, Saleem Ghantous, Elad Feldman

CoRR, 2023

MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistance with Transformers.

Nadav Schneider, Tal Kadosh, Niranjan Hasabnis, Timothy G. Mattson, Yuval Pinter, Gal Oren

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Advising OpenMP Parallelization via A Graph-Based Approach with Transformers.

Tal Kadosh, Nadav Schneider, Niranjan Hasabnis, Timothy G. Mattson, Yuval Pinter, Gal Oren

Proceedings of the OpenMP: Advanced Task-Based, Device and Compiler Programming, 2023

2022

Determining HEDP Foams' Quality with Multi-View Deep Learning Classification.

Nadav Schneider, Matan Rusanovsky, Raz Gvishi, Gal Oren

Proceedings of the IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, 2022