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- Wentao ZhangAffiliation: Peking University, Center of Machine Learning ResearchWork: DeepSeek-V3 Technical Report
- Andy DavisAffiliation: GoogleWork: EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration
- Duc Dung NguyenAffiliation: Not recordedWork: The Machine Learning Behind Hum to Search
- Evan HubingerAffiliation: AnthropicWork: Risks from Learned Optimization in Advanced Machine Learning Systems
- Evan MaysAffiliation: Not recordedWork: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
- Florian TramèrAffiliation: ETH ZürichWork: Stealing Machine Learning Models via Prediction APIs
- Harkirat BehlAffiliation: MicrosoftWork: ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments
- Martin GörnerAffiliation: Axelera AIWork: Practical Machine Learning for Computer Vision
- Martin WickeAffiliation: Not recordedWork: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Matthew JagielskiAffiliation: AnthropicWork: Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
- Omar AjmeriAffiliation: GoogleWork: Using Computer Vision and Machine Learning to Automatically Classify NFL Game Film and Develop a Player Tracking System
- Polina ZvyaginaAffiliation: General MotorsWork: System-Level Transparency of Machine Learning
- Qian HuangAffiliation: Not recordedWork: MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
- Shai Shalev-ShwartzAffiliation: MobileyeWork: Understanding Machine Learning: From Theory to Algorithms
- Tom HenniganAffiliation: Not recordedWork: TF-Replicator: Distributed Machine Learning for Researchers
- Vijay VasudevanAffiliation: MintMCPWork: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Abhishek GoswamiAffiliation: Microsoft ResearchWork: Machine Learning at Microsoft with ML.NET
- Aedan PopeAffiliation: Not recordedWork: Launchpad: A Programming Model for Distributed Machine Learning Research
- Ajit MathewsAffiliation: Not recordedWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Alban DesmaisonAffiliation: MetaWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Alban RrustemiAffiliation: Not recordedWork: Launchpad: A Programming Model for Distributed Machine Learning Research
- Alexei RobskyAffiliation: MicrosoftWork: Machine Learning Governance for Managers
- Alina OpreaAffiliation: Northeastern UniversityWork: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
- Alireza GhaffarkhahAffiliation: Not recordedWork: Resiliency at Scale: Managing Google's TPUv4 Machine Learning Supercomputer
- Ankur GargAffiliation: Not recordedWork: Asynchronously training machine learning models across client devices for adaptive intelligence
- Athyuttam EletiAffiliation: Not recordedWork: Systems and methods for generating and executing function calls using machine learning
- Barry (Xuanyi) DongAffiliation: Not recordedWork: PyGlove: Symbolic Programming for Automated Machine Learning
- Bart ChrzaszczAffiliation: Google DeepMindWork: PartIR: Composing SPMD Partitioning Strategies for Machine Learning
- Ben HutchinsonAffiliation: Google ResearchWork: Evaluation Gaps in Machine Learning Practice
- Bhargava Urala KotaAffiliation: Not recordedWork: ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data
- Binh TangAffiliation: NetflixWork: A Theory on Adam Instability in Large-Scale Machine Learning
- Brendan Dolan-GavittAffiliation: XBOWWork: BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
- Chan Jun ShernAffiliation: AnthropicWork: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
- Chetan TekurAffiliation: Reflection AIWork: A software-defined tensor streaming multiprocessor for large-scale machine learning
- Christina GreerAffiliation: Not recordedWork: Evaluation Gaps in Machine Learning Practice
- Clément FarabetAffiliation: Google DeepMindWork: Torch7: A Matlab-like Environment for Machine Learning
- Cristian Canton FerrerAffiliation: Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS)Work: On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms
- Daiyi PengAffiliation: GoogleWork: PyGlove: Symbolic Programming for Automated Machine Learning
- Dane SherburnAffiliation: P-Zero ResearchWork: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
- Daniel Voigt GodoyAffiliation: Linux Foundation and Data Science RetreatWork: Understanding Machine Learning
- David A. W. SoergelAffiliation: GoogleWork: TensorFlow.js: Machine Learning for the Web and Beyond
- David AdkinsAffiliation: Not recordedWork: System-Level Transparency of Machine Learning
- David W. Sculley IIAffiliation: Not recordedWork: Hidden Technical Debt in Machine Learning Systems
- Dawn SongAffiliation: MetaWork: Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
- Dayou DuAffiliation: Not recordedWork: Resiliency at Scale: Managing Google’s TPUv4 Machine Learning Supercomputer
- Denis VnukovAffiliation: Not recordedWork: EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration
- Diana MincuAffiliation: Not recordedWork: Underspecification Presents Challenges for Credibility in Modern Machine Learning
- Divyashree Shivakumar SreepathihalliAffiliation: GoogleWork: Using KerasHub for easy end-to-end machine learning workflows with Hugging Face
- Dominik GreweAffiliation: Not recordedWork: PartIR: Composing SPMD Partitioning Strategies for Machine Learning
- Edward BermanAffiliation: Harvard UniversityWork: The State of Julia for Scientific Machine Learning
- Edward Z. YangAffiliation: MetaWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Emily CavenessAffiliation: Not recordedWork: Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines
- Eric P. XingAffiliation: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Work: Petuum: A New Platform for Distributed Machine Learning on Big Data
- Fantine HuotAffiliation: Google DeepMindWork: Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data
- Fei SunAffiliation: Not recordedWork: Machine Learning at Facebook: Understanding Inference at the Edge
- Gabriela SuritaAffiliation: Google DeepMindWork: Resolving Code Review Comments with Machine Learning
- Geeta ChauhanAffiliation: MetaWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Geoffrey IrvingAffiliation: ResolutionWork: TensorFlow: A system for large-scale machine learning
- Giulio StaraceAffiliation: ParadigmaWork: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
- Gökay AydoğanAffiliation: fal.aiWork: Physics-based machine learning for modeling of laminated composite plates based on refined zigzag theory
- Helen SukAffiliation: Not recordedWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Hila NogaAffiliation: Not recordedWork: Flood forecasting with machine learning models in an operational framework
- Hoang Long LeAffiliation: StudiosityWork: Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models
- Hongyu RenAffiliation: Not recordedWork: Open Graph Benchmark: Datasets for Machine Learning on Graphs
- Horace HeAffiliation: Thinking Machines LabWork: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
- Hyeontaek LimAffiliation: Google DeepMindWork: 3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning
- Ido BlassAffiliation: Bar-Ilan UniversityWork: Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach
- Igor MolybogAffiliation: University of Hawaiʻi at MānoaWork: A Theory on Adam Instability in Large-Scale Machine Learning
- Iuliia VitiugovaAffiliation: Not recordedWork: Electronic Nose Machine Learning Research
- James AungAffiliation: AI Security Institute (AISI), United KingdomWork: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
- James MolloyAffiliation: Not recordedWork: PartIR: Composing SPMD Partitioning Strategies for Machine Learning
- Jason GelmanAffiliation: JPMorganChaseWork: Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
- Jasper SnoekAffiliation: Google DeepMindWork: Practical Bayesian Optimization of Machine Learning Algorithms
- Jaymin BhanAffiliation: Seoul National University of Science and Technology (SeoulTech)Work: Kaggle Competition — Temperature Forecasting (SeoulTech Machine Learning Class)
- Jeff DeanAffiliation: Discovery LoopWork: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Jenny HongAffiliation: MetaWork: The Recon Approach: A New Direction for Machine Learning in Criminal Law
- Jiří ŠimšaAffiliation: Google DeepMindWork: tf.data: A Machine Learning Data Processing Framework
- Jitesh Vinod PunjabiAffiliation: Not recordedWork: Relaxed Context-Aware Machine Learning Middleware (RCAMM) for Android: A Step towards Sustainability
- Josh GordonAffiliation: Not recordedWork: Machine Learning: Recipes for New Developers
- Juliana Patrícia Vicente FrancoAffiliation: Google DeepMindWork: PartIR: Composing SPMD Partitioning Strategies for Machine Learning