Fuentes de referencia

Referencias 673–696 de 766

673. The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing

     Tyler Akidau; Robert Bradshaw; Craig Chambers; Slava Chernyak; Rafael J. Fernández-Moctezuma; Reuven Lax; Sam McVeety; Daniel Mills; Frances Perry; Eric Schmidt; Sam Whittle
     2015
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     • When a Stream Is Complete: Event Time, Watermarks, and Revisable Results

674. The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

     Ilyes Batatia; Simon Batzner; Dávid Péter Kovács; Albert Musaelian; Gregor N. C. Simm; Ralf Drautz; Christoph Ortner; Boris Kozinsky; Gábor Csányi
     2022
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     • Handedness Is Information: Molecular Equivariance Beyond Distances

675. The Entity Attestation Token (EAT)

     Laurence Lundblade; Giridhar Mandyam; Jeremy O'Donoghue; Carl Wallace
     2025
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     • What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary

676. The generalization error of random features regression: Precise asymptotics and double descent curve

     Song Mei; Andrea Montanari
     2020
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     • When Fitting Noise Is Benign: Interpolation, Effective Rank, and the Geometry of Prediction

677. The I-JSON Message Format (RFC 7493)

     Tim Bray
     2015
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     • One Message, Two Languages: Parser Differentials as a Security Boundary

678. The impact of site-specific digital histology signatures on deep learning model accuracy and bias

     Frederick M. Howard; James Dolezal; Sara Kochanny; Jefree Schulte; Heather Chen; Lara Heij; Dezheng Huo; Rita Nanda; Olufunmilayo I. Olopade; Jakob N. Kather; Nicole Cipriani; Robert L. Grossman; Alexander T. Pearson
     2021
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     • From Slides to Sites: Foundation Models, Distribution Shift and Histopathology Validation

679. The implicit bias of gradient descent on nonseparable data

     Ziwei Ji; Matus Telgarsky
     2019
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     • Zero Training Error Does Not Determine the Learned Classifier

680. The Implicit Bias of Gradient Descent on Separable Data

     Daniel Soudry; Elad Hoffer; Mor Shpigel Nacson; Suriya Gunasekar; Nathan Srebro
     2018
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     • Zero Training Error Does Not Determine the Learned Classifier

681. The JavaScript Object Notation (JSON) Data Interchange Format (RFC 8259)

     Tim Bray
     2017
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     • One Message, Two Languages: Parser Differentials as a Security Boundary

682. The Log-Structured Merge-Tree (LSM-Tree)

     Patrick O’Neil; Edward Cheng; Dieter Gawlick; Elizabeth O’Neil
     1996
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     • The Price of Sorted State: Compaction, Amplification, and Value Separation

683. The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds

     Paolo Ferragina; Giorgio Vinciguerra
     2020
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     • Learned Indexes Under Distribution Change: Prediction, Search, and Correctness

684. The Risks of Invariant Risk Minimization

     Elan Rosenfeld; Pradeep Ravikumar; Andrej Risteski
     2021
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     • Auditing the Shortcut: Feature Interventions, Hidden Groups, and the Limits of Robustness Claims

685. The SyGuS Language Standard Version 2.1

     Saswat Padhi; Elizabeth Polgreen; Mukund Raghothaman; Andrew Reynolds; Abhishek Udupa
     2021
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     • Programs from Counterexamples: Synthesis, Specifications, and Oracle Limits

686. The tail at scale

     Jeffrey Dean; Luiz André Barroso
     2013
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     • The Geometry of Tail Latency: Fan-Out, Queueing, and Redundant Work

687. The Transit Light Source Effect: False Spectral Features and Incorrect Densities for M-dwarf Transiting Planets

     Benjamin V. Rackham; Dániel Apai; Mark S. Giampapa
     2018
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     • An Atmosphere through a Spectrum: Clouds, Stellar Contamination and Exoplanet Retrieval

688. The Transport Layer Security (TLS) Protocol Version 1.3

     Eric Rescorla
     2018
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     • Replacing a Primitive Is Not Migrating a Protocol: Post-Quantum Key Establishment

689. The upper bound revisited

     Lloyd M. Robeson
     2008
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     • Separating Molecules at Scale: Membrane Selectivity, Permeance and Process-Level Value

690. Theoretically Principled Trade-off between Robustness and Accuracy

     , , , , ,
     2019-01-24

     Ubicación en la fuente: Abstract; 原文方法、理论和实验章节见作者原始论文链接

     Alcance de la consulta: 一手论文；原文研究归属原作者。本文的推导示例与建议实验方案另行标注，不冒充原论文或本站实测。

     Consulta: 2026-10-21

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     • 对抗鲁棒性的可检验边界：威胁模型、范数预算与攻击验收

691. Thermoelectric Efficiency and Compatibility

     G. Jeffrey Snyder; Tristan S. Ursell
     2003
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     • From a Large Figure of Merit to Useful Power: Thermoelectric Materials under Real Constraints

692. Tighter Risk Certificates for Neural Networks

     María Pérez-Ortiz; Omar Rivasplata; John Shawe-Taylor; Csaba Szepesvári
     2021
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     • Who Chose the Prior? PAC-Bayes Certificates, Data Dependence, and the Cost of Selection

693. Time2Vec: Learning a Vector Representation of Time

     Seyed Mehran Kazemi; Rishab Goel; Sepehr Eghbali; Janahan Ramanan; Jaspreet Sahota; Sanjay Thakur; Stella Wu; Cathal Smyth; Pascal Poupart; Marcus Brubaker
     2019
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     • Forecasts Must Respect the Clock: Temporal Leakage and Evaluation

694. Timely Stream Processing

     Apache Flink contributors
     2026
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     • When a Stream Is Complete: Event Time, Watermarks, and Revisable Results

695. TIMELY: RTT-based Congestion Control for the Datacenter

     Radhika Mittal; Vinh The Lam; Nandita Dukkipati; Emily Blem; Hassan Wassel; Monia Ghobadi; Amin Vahdat; Yaogong Wang; David Wetherall; David Zats
     2015
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     • Congestion Control as Delayed Inference: Throughput, Queues, and Fairness

696. TinyLFU: A Highly Efficient Cache Admission Policy

     Gil Einziger; Roy Friedman; Ben Manes
     2017
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     • Caching Beyond Recency: Admission, Workload Shift, and the Cost of a Miss