Copertina

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zulkifli, Kahfi S., Qian, Wenbo, Zhu, Shaowei, Zhou, Yuan, Zhang, Zhen, Lou, Chang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Programming Languages
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
di: Pezeshkpour, Pouya, et al.
Pubblicazione: (2026)

Verified Lifting of Deep learning Operators
di: Zhan, Qi, et al.
Pubblicazione: (2024)

Faster Verified Explanations for Neural Networks
di: De Palma, Alessandro, et al.
Pubblicazione: (2025)

DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing
di: Boudaoud, Afif, et al.
Pubblicazione: (2025)

A Formally Verified Robustness Certifier for Neural Networks (Extended Version)
di: Tobler, James, et al.
Pubblicazione: (2025)

ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning
di: Yang, Chengcao
Pubblicazione: (2026)

VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers
di: Qi, Jianing, et al.
Pubblicazione: (2024)

SEVerA: Verified Synthesis of Self-Evolving Agents
di: Banerjee, Debangshu, et al.
Pubblicazione: (2026)

DVM: A Bytecode Virtual Machine Approach for Dynamic Tensor Computation
di: Fang, Jingzhi, et al.
Pubblicazione: (2026)

A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning
di: Navarro, Alejandro L. García, et al.
Pubblicazione: (2024)

Large Language Models Synergize with Automated Machine Learning
di: Xu, Jinglue, et al.
Pubblicazione: (2024)

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
di: Weng, Han, et al.
Pubblicazione: (2025)

On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study
di: Mir, Amir M., et al.
Pubblicazione: (2024)

Linguacodus: A Synergistic Framework for Transformative Code Generation in Machine Learning Pipelines
di: Trofimova, Ekaterina, et al.
Pubblicazione: (2024)

The State of Julia for Scientific Machine Learning
di: Berman, Edward, et al.
Pubblicazione: (2024)

Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
di: Chen, Silin, et al.
Pubblicazione: (2024)

FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
di: Ravi, Nikil, et al.
Pubblicazione: (2026)

Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
di: Lai, Ruihang, et al.
Pubblicazione: (2023)

SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention
di: Gupta, Ahan, et al.
Pubblicazione: (2024)

Recursive Function Definitions in Static Dataflow Graphs and their Implementation in TensorFlow
di: Kostopoulou, Kelly, et al.
Pubblicazione: (2024)

EAGLE-Pangu: Accelerator-Safe Tree Speculative Decoding on Ascend NPUs
di: Han, Chang, et al.
Pubblicazione: (2026)

Self-Infilling Code Generation
di: Zheng, Lin, et al.
Pubblicazione: (2023)

NaN-Propagation: A Novel Method for Sparsity Detection in Black-Box Computational Functions
di: Sharpe, Peter
Pubblicazione: (2025)

depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
di: You, Kaichao, et al.
Pubblicazione: (2024)

MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
di: Notton, Cassandre, et al.
Pubblicazione: (2026)

FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow
di: Lacouture, Rubens, et al.
Pubblicazione: (2025)

PolyBlocks: A Compiler Infrastructure for AI Chips and Programming Frameworks
di: Bondhugula, Uday, et al.
Pubblicazione: (2026)

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach
di: Dong, Shouyang, et al.
Pubblicazione: (2025)

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation
di: Liu, Yiqun, et al.
Pubblicazione: (2026)

Learning Minimal Neural Specifications
di: Geng, Chuqin, et al.
Pubblicazione: (2024)

Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
di: Boruch-Gruszecki, Aleksander, et al.
Pubblicazione: (2025)

Verifying Chain-of-Thought Reasoning via Its Computational Graph
di: Zhao, Zheng, et al.
Pubblicazione: (2025)

Learning Structure-Aware Representations of Dependent Types
di: Kogkalidis, Konstantinos, et al.
Pubblicazione: (2024)

A Deep Learning Model for Predicting Transformation Legality
di: Tiwari, Avani, et al.
Pubblicazione: (2025)

MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
di: Yu, Xingtong, et al.
Pubblicazione: (2023)

GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
di: Kashmira, Savini, et al.
Pubblicazione: (2025)

Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization
di: Marinov, Teodor V., et al.
Pubblicazione: (2024)

Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning
di: Alon, Yoav, et al.
Pubblicazione: (2024)

Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
di: Qin, Zhanyue, et al.
Pubblicazione: (2026)

CompilerDream: Learning a Compiler World Model for General Code Optimization
di: Deng, Chaoyi, et al.
Pubblicazione: (2024)