activity
20222026
most citedScope is all you need: Transforming LLMs for HPC Code

2 citations · 6 across the 10 of their papers we have counts for

collaborators

11 papers

quant-ph2026

Distance Prediction for Quantum Codes

Nadav Schneider, Eliya Nachmani

The distance of a quantum code is a fundamental metric that dictates its error-correction capability. While quantum low-density parity-check (qLDPC) families such as Bivariate Bicy…

cs.LG2025

Differential Mamba

Nadav Schneider, Itamar Zimerman, Eliya Nachmani

Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by prom…

cs.NE2024

Reactor Optimization Benchmark by Reinforcement Learning

Deborah Schwarcz, Nadav Schneider, Gal Oren +1

Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays mor…

cs.DC2024

MPIrigen: MPI Code Generation through Domain-Specific Language Models

Nadav Schneider, Niranjan Hasabnis, Vy A. Vo +9

The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (…

cs.PL2023

MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +10

With easier access to powerful compute resources, there is a growing trend in AI for software development to develop large language models (LLMs) to address a variety of programmin…

cs.CL2023★ 2 cited

Scope is all you need: Transforming LLMs for HPC Code

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +9

With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to addres…

cs.CV2023

Explainable Multi-View Deep Networks Methodology for Experimental Physics

Nadav Schneider, Muriel Tzdaka, Galit Sturm +5

Physical experiments often involve multiple imaging representations, such as X-ray scans and microscopic images. Deep learning models have been widely used for supervised analysis…

cs.DC2023★ 1 cited

Advising OpenMP Parallelization via a Graph-Based Approach with Transformers

Tal Kadosh, Nadav Schneider, Niranjan Hasabnis +3

There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing th…

cs.DC2023★ 1 cited

MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers

Nadav Schneider, Tal Kadosh, Niranjan Hasabnis +3

Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, perfor…

cs.CL2023★ 2 cited

Hate Speech Targets Detection in Parler using BERT

Nadav Schneider, Shimon Shouei, Saleem Ghantous +1

Online social networks have become a fundamental component of our everyday life. Unfortunately, these platforms are also a stage for hate speech. Popular social networks have regul…

cs.CV2022

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

Nadav Schneider, Matan Rusanovsky, Raz Gvishi +1

High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a low-density foam. This effect affects its density and hence, its transpare…