Imagen de Portada

AI Benchmark Democratization and Carpentry

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	von Laszewski, Gregor, Brewer, Wesley, Thiyagalingam, Jeyan, Papay, Juri, Foundjem, Armstrong, Luszczek, Piotr, Emani, Murali, Moore, Shirley V., Reddi, Vijay Janapa, Sinclair, Matthew D., Lobentanzer, Sebastian, Goswami, Sujata, Hawks, Benjamin, Colombo, Marco, Tran, Nhan, Kirkpatrick, Christine R., Alsudais, Abdulkareem, Barrett, Gregg, Li, Tianhao, Morehouse, Kirsten, Venkataraman, Shivaram, Jain, Rutwik, Mathur, Kartik, Lu, Victor, Singh, Tejinder, Mirza, Khojasteh Z., Chen, Kongtao, Kunapuli, Sasidhar, Farrell, Gavin, Umeton, Renato, Fox, Geoffrey C.
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence I.2.6
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploring the change in scientific readability following the release of ChatGPT
por: Alsudais, Abdulkareem
Publicado: (2025)

Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities
por: Alsudais, Abdulkareem
Publicado: (2025)

An MLCommons Scientific Benchmarks Ontology
por: Hawks, Ben, et al.
Publicado: (2025)

MLCommons Cloud Masking Benchmark with Early Stopping
por: Chennamsetti, Varshitha, et al.
Publicado: (2023)

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters
por: Jain, Rutwik, et al.
Publicado: (2026)

TinyTorch: Building Machine Learning Systems from First Principles
por: Reddi, Vijay Janapa
Publicado: (2026)

Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
por: Allotey, Johannes, et al.
Publicado: (2021)

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
por: Leng, Kuangdai, et al.
Publicado: (2023)

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
por: Jain, Rutwik, et al.
Publicado: (2024)

Enhancing Energy Efficiency in Scientific Workflows through CFD based PIVAEs
por: Zahir, Ali, et al.
Publicado: (2026)

NL2GDS: LLM-aided interface for Open Source Chip Design
por: Eland, Max, et al.
Publicado: (2026)

Generative AI Agents in Autonomous Machines: A Safety Perspective
por: Jabbour, Jason, et al.
Publicado: (2024)

Continuous Symmetry Discovery and Enforcement Using Infinitesimal Generators of Multi-parameter Group Actions
por: Shaw, Ben, et al.
Publicado: (2025)

Deep Learning Evidence for Global Optimality of Gerver's Sofa
por: Leng, Kuangdai, et al.
Publicado: (2024)

Differentially Private Wasserstein Barycenters
por: Gu, Anming, et al.
Publicado: (2025)

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
por: Chang, Tzu-Tao, et al.
Publicado: (2025)

Eva: Cost-Efficient Cloud-Based Cluster Scheduling
por: Chang, Tzu-Tao, et al.
Publicado: (2025)

Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
por: Gaur, Mudit, et al.
Publicado: (2025)

The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems
por: Neuman, Sabrina M., et al.
Publicado: (2024)

Deep learning insights into cosmological structure formation
por: Lucie-Smith, Luisa, et al.
Publicado: (2020)

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification
por: Ige, Tosin, et al.
Publicado: (2025)

Scaling Inference-Efficient Language Models
por: Bian, Song, et al.
Publicado: (2025)

Decoding Speculative Decoding
por: Yan, Minghao, et al.
Publicado: (2024)

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices
por: Yan, Minghao, et al.
Publicado: (2023)

Quantifying the Expectation-Realisation Gap for Agentic AI Systems
por: Lobentanzer, Sebastian
Publicado: (2026)

The Agentic Automation Canvas: a structured framework for agentic AI project design
por: Lobentanzer, Sebastian
Publicado: (2026)

Ein geisteswissenschaftliches Curriculum für The Carpentries
por: Berg, Corinna, et al.
Publicado: (2025)

What Are "The Carpentries" and What Are They Doing in the Library?
por: Pugachev, Sarah
Publicado: (2019)

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
por: Ma, Jeffrey, et al.
Publicado: (2024)

From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads
por: Kanellis, Konstantinos, et al.
Publicado: (2023)

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
por: Bian, Song, et al.
Publicado: (2025)

GraphSnapShot: Caching Local Structure for Fast Graph Learning
por: Liu, Dong, et al.
Publicado: (2024)

ARMS: Adaptive and Robust Memory Tiering System
por: Yadalam, Sujay, et al.
Publicado: (2025)

TUNA: Tuning Unstable and Noisy Cloud Applications
por: Freischuetz, Johannes, et al.
Publicado: (2025)

SYMPHONY: Improving Memory Management for LLM Inference Workloads
por: Agarwal, Saurabh, et al.
Publicado: (2024)

SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility
por: Jabbour, Jason, et al.
Publicado: (2025)

Dinaturality for Double Categories
por: Morehouse, Edward
Publicado: (2026)

Compilation of Bibliographies on Southeast Asia and the Middle East and North Africa for Undergraduate Libraries. Final Report.
por: Morehouse, Ward
Publicado: (1971)

Tesserae: Scalable Placement Policies for Deep Learning Workloads
por: Bian, Song, et al.
Publicado: (2025)

Augmented Carpentry: Computer Vision-assisted Framework for Manual Fabrication
por: Settimi, Andrea, et al.
Publicado: (2025)