Fuentes de referencia

Referencias 601–624 de 766

601. Secure Single-Server Vector Aggregation with (Poly)Logarithmic Overhead

     James Bell; K. A. Bonawitz; Adria Gascon; Tancrede Lepoint; Mariana Raykova
     2020
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     • A Sum without Its Summands: Secure Aggregation under Dropout and Collusion

602. Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

     Jinhyun So; Ramy E. Ali; Basak Guler; Jiantao Jiao; Salman Avestimehr
     2023
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     • A Sum without Its Summands: Secure Aggregation under Dropout and Collusion

603. Segment Anything

     , , , , , , , , , , ,
     2023-04-05

     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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     • 多模态表征的可迁移性：对比目标、信息瓶颈与跨分布失效
     • 可提示分割与视频记忆：SAM 接口、掩码歧义和跨域验证

604. Segregation of mitochondrial DNA heteroplasmy through a developmental genetic bottleneck in human embryos

     Vasileios I. Floros; Angela Pyle; Sabine Dietmann; Wei Wei; Walfred C. W. Tang; Naoko Irie; Brendan Payne; Antonio Capalbo; Laila Noli; Jonathan Coxhead; Gavin Hudson; Moira Crosier; Henrik Strahl; Yacoub Khalaf; Mitinori Saitou; Dusko Ilic; M. Azim Surani; Patrick F. Chinnery
     2018
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     • Genomes within Cells: Drift, Selection and the Measurement of Mitochondrial Heteroplasmy

605. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

     Zhu et al.
     2023

     Ubicación en la fuente: Temporal-spatial DAS picker with pseudo-labels and phase association; sensor-domain distinction.

     Consulta: 2026-10-22

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     • 从波形检测到地震目录：学习拾取、关联定位与完备性的联合分析

606. seL4: formal verification of an OS kernel

     Gerwin Klein; Kevin Elphinstone; Gernot Heiser; June Andronick; David Cock; Philip Derrin; Dhammika Elkaduwe; Kai Engelhardt; Rafal Kolanski; Michael Norrish; Thomas Sewell; Harvey Tuch; Simon Winwood
     2009
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     • What a Verified System Actually Guarantees: Refinement and the Trusted Boundary

607. Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR–Cas gene editing

     Qiang Cheng; Tuo Wei; Lukas Farbiak; Lindsay T. Johnson; Sean A. Dilliard; Daniel J. Siegwart
     2020
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     • Beyond Encapsulation: The Multistage Transport Problem of Messenger RNA Delivery

608. Self-Consistency Improves Chain of Thought Reasoning in Language Models

     Wang et al.
     2022-03-21

     Ubicación en la fuente: v4 abstract

     Alcance de la consulta: Samples multiple reasoning paths and marginalizes to choose a consistent answer. Correlated model errors can invalidate independent-vote intuitions.

     Consulta: 2026-10-15

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     • 语言模型推理的验证边界：采样、搜索与正确性证据

609. Self-Consuming Generative Models Go MAD

     Sina Alemohammad; Josue Casco-Rodriguez; Lorenzo Luzi; Ahmed Imtiaz Humayun; Hossein Babaei; Daniel LeJeune; Ali Siahkoohi; Richard G. Baraniuk
     2023
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     • Recursive Synthetic Data and the Fate of Rare Support

610. Self-Instruct: Aligning Language Models with Self-Generated Instructions

     , , , , , ,
     2022-12-20

     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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     • 合成训练数据的质量闭环：覆盖、筛选偏差与尾部信息流失

611. Self-Instruct: Aligning Language Models with Self-Generated Instructions

     Yizhong Wang; Yeganeh Kordi; Swaroop Mishra; Alisa Liu; Noah A. Smith; Daniel Khashabi; Hannaneh Hajishirzi
     2022
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     • Recursive Synthetic Data and the Fate of Rare Support

612. Self-Paced Learning for Latent Variable Models

     M. Pawan Kumar; Benjamin Packer; Daphne Koller
     2010
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     • A Curriculum Is Also a Sampling Policy

613. Self-training Improves Pre-training for Natural Language Understanding

     Jingfei Du; Edouard Grave; Beliz Gunel; Vishrav Chaudhary; Onur Celebi; Michael Auli; Ves Stoyanov; Alexis Conneau
     2020
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     • Recursive Synthetic Data and the Fate of Rare Support

614. Semantics-Guided Synthesis

     Jinwoo Kim; Qinheping Hu; Loris D'Antoni; Thomas Reps
     2020
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     • Programs from Counterexamples: Synthesis, Specifications, and Oracle Limits

615. SemDeDup: Data-efficient learning at web-scale through semantic deduplication

     Amro Abbas; Kushal Tirumala; Dániel Simig; Surya Ganguli; Ari S. Morcos
     2023
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     • Data Multiplicity Is a Training Measure

616. Semi-Supervised Classification with Graph Convolutional Networks

     ,
     2016-09-09

     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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     • 图神经网络的表达与传播：聚合可辨性、平滑和评估泄漏

617. Semi-Supervised Classification with Graph Convolutional Networks

     Thomas N. Kipf; Max Welling
     2017
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     • Graph Depth, Smoothing, and the Loss of Distinctions

618. SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

     Taku Kudo; John Richardson
     2018
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     • Tokens Are Not Units of Meaning: Probability Accounting Across Segmentations

619. Serializable snapshot isolation in PostgreSQL

     Dan R. K. Ports; Kevin Grittner
     2012
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     • Serializable Transactions as an Invariant-Preservation Problem

620. Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

     Mohammad Shahrad; Rodrigo Fonseca; Inigo Goiri; Gohar Chaudhry; Paul Batum; Jason Cooke; Eduardo Laureano; Colby Tresness; Mark Russinovich; Ricardo Bianchini
     2020
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     • Serverless Is a Placement Decision: Cold Starts, Isolation, and State

621. SEV Secure Nested Paging Firmware ABI Specification

     Advanced Micro Devices
     2025
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     • What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary

622. Shallow-Deep Networks: Understanding and Mitigating Network Overthinking

     Yigitcan Kaya; Sanghyun Hong; Tudor Dumitras
     2019
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     • The Price of Stopping Early: Adaptive Depth, Confidence, and the Systems Cost of Neural Decisions

623. Sharp Minima Can Generalize For Deep Nets

     Laurent Dinh; Razvan Pascanu; Samy Bengio; Yoshua Bengio
     2017
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     • The Same Predictor, a Sharper Minimum: Parameter Symmetry and the Meaning of Loss Geometry

624. Sharpness-Aware Minimization for Efficiently Improving Generalization

     Pierre Foret; Ariel Kleiner; Hossein Mobahi; Behnam Neyshabur
     2021
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     • The Same Predictor, a Sharper Minimum: Parameter Symmetry and the Meaning of Loss Geometry