Reference sources

References 361–384 of 766

361. Improved Techniques for Training Consistency Models

     Yang Song; Prafulla Dhariwal
     2023
     Open original source ↗
     Referenced in (1)

     • One Step Is a Budget, Not a Distribution: Consistency Models and the Evidence for Fast Generation

362. Improving language models by retrieving from trillions of tokens

     Sebastian Borgeaud; Arthur Mensch; Jordan Hoffmann; Trevor Cai; Eliza Rutherford; Katie Millican; George van den Driessche; Jean-Baptiste Lespiau; Bogdan Damoc; Aidan Clark; Diego de Las Casas; Aurelia Guy; Jacob Menick; Roman Ring; Tom Hennigan; Saffron Huang; Loren Maggiore; Chris Jones; Albin Cassirer; Andy Brock; Michela Paganini; Geoffrey Irving; Oriol Vinyals; Simon Osindero; Karen Simonyan; Jack W. Rae; Erich Elsen; Laurent Sifre
     2021
     Open original source ↗
     Referenced in (1)

     • Retrieval Is Part of the Model: Exposure, Evidence, and Training-Time Coupling

363. Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising

     Borja Balle; Yu-Xiang Wang
     2018
     Open original source ↗
     Referenced in (1)

     • Privacy Budgets Under Composition: Accounting for Repeated Statistical Release

364. In Search of an Understandable Consensus Algorithm

     Diego Ongaro; John Ousterhout
     2014
     Open original source ↗
     Referenced in (1)

     • Consensus Beyond the Happy Path: Safety, Progress, and the Cost of Quorums

365. In Search of Lost Domain Generalization

     Ishaan Gulrajani; David Lopez-Paz
     2020
     Open original source ↗
     Referenced in (1)

     • Auditing the Shortcut: Feature Interventions, Hidden Groups, and the Limits of Robustness Claims

366. In-context Learning and Induction Heads

     Catherine Olsson; Nelson Elhage; Neel Nanda; Nicholas Joseph; Nova DasSarma; Tom Henighan; Ben Mann; Amanda Askell; Yuntao Bai; Anna Chen; Tom Conerly; Dawn Drain; Deep Ganguli; Zac Hatfield-Dodds; Danny Hernandez; Scott Johnston; Andy Jones; Jackson Kernion; Liane Lovitt; Kamal Ndousse; Dario Amodei; Tom Brown; Jack Clark; Jared Kaplan; Sam McCandlish; Chris Olah
     2022
     Open original source ↗
     Referenced in (1)

     • Learning Inside the Context: Bayesian Ambiguity and Algorithmic Imitation

367. in-toto: Providing farm-to-table guarantees for bits and bytes

     Santiago Torres-Arias; Hammad Afzali; Trishank Karthik Kuppusamy; Reza Curtmola; Justin Cappos
     2019
     Open original source ↗
     Referenced in (1)

     • From Source to Artifact: Reproducible Builds and the Limits of Provenance

368. Inductive Representation Learning on Large Graphs

     , ,
     2017-06-07

     Source location: Abstract; 原文方法、理论和实验章节见作者原始论文链接

     Source scope: 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。

     Inspected on: 2026-10-21

     Open original source ↗
     Referenced in (1)

     • 图神经网络的表达与传播：聚合可辨性、平滑和评估泄漏

369. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

     Haoyi Zhou; Shanghang Zhang; Jieqi Peng; Shuai Zhang; Jianxin Li; Hui Xiong; Wancai Zhang
     2020
     Open original source ↗
     Referenced in (1)

     • Forecasts Must Respect the Clock: Temporal Leakage and Evaluation

370. Intel SGX and TDX DCAP Quote Verification Service

     Intel
     2026
     Open original source ↗
     Referenced in (1)

     • What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary

371. Intel SGX Explained

     Victor Costan; Srinivas Devadas
     2016
     Open original source ↗
     Referenced in (1)

     • What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary

372. Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

     Marco Gerlinger; Andrew J. Rowan; Stuart Horswell; James Larkin; David Endesfelder; Eva Gronroos; Pierre Martinez; Nicholas Matthews; Aengus Stewart; Patrick Tarpey; Ignacio Varela; Benjamin Phillimore; Sharmin Begum; Neil Q. McDonald; Adam Butler; David Jones; Keiran Raine; Calli Latimer; Claudio R. Santos; Mahrokh Nohadani; Aron C. Eklund; Bradley Spencer-Dene; Graham Clark; Lisa Pickering; Gordon Stamp; Martin Gore; Zoltan Szallasi; Julian Downward; P. Andrew Futreal; Charles Swanton
     2012
     Open original source ↗
     Referenced in (1)

     • The Tumor Is Not a Sample: Spatial Evolution, Detection Limits and Longitudinal Inference

373. Intraventricular CARv3-TEAM-E T Cells in Recurrent Glioblastoma

     Bryan D. Choi; Elizabeth R. Gerstner; Matthew J. Frigault; Mark B. Leick; Christopher W. Mount; Leonora Balaj; Sarah Nikiforow; Bob S. Carter; William T. Curry; Kathleen Gallagher; Marcela V. Maus
     2024
     Open original source ↗
     Referenced in (1)

     • Recognition Is Only the Beginning: Engineering Cell Therapies across Solid-Tumor Barriers

374. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

     Armen Aghajanyan; Luke Zettlemoyer; Sonal Gupta
     2020
     Open original source ↗
     Referenced in (1)

     • What Low-Rank Adaptation Can and Cannot Represent

375. Invariant Risk Minimization

     , , ,
     2019-07-05

     Source location: Abstract; 原文方法、理论和实验章节见作者原始论文链接

     Source scope: 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。

     Inspected on: 2026-10-21

     Open original source ↗
     Referenced in (1)

     • 从相关到干预：AI 因果推断中的识别、混杂与可迁移性

376. Invariant Risk Minimization

     Martin Arjovsky; Léon Bottou; Ishaan Gulrajani; David Lopez-Paz
     2019
     Open original source ↗
     Referenced in (1)

     • Generalization Under Shift Requires Identifiable Assumptions

377. Invariant Risk Minimization

     Martin Arjovsky; Léon Bottou; Ishaan Gulrajani; David Lopez-Paz
     2019
     Open original source ↗
     Referenced in (1)

     • Auditing the Shortcut: Feature Interventions, Hidden Groups, and the Limits of Robustness Claims

378. Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing

     Sved et al.
     2026-03-04

     Source location: Three-dimensional FDTD design and measured compact optical classifiers; density does not equal general-purpose throughput.

     Inspected on: 2026-10-22

     Open original source ↗
     Referenced in (1)

     • 光子器件的 AI 逆向设计：非唯一映射、全波验证与制造鲁棒性

379. IronFleet: proving practical distributed systems correct

     Chris Hawblitzel; Jon Howell; Manos Kapritsos; Jacob R. Lorch; Bryan Parno; Michael L. Roberts; Srinath Setty; Brian Zill
     2015
     Open original source ↗
     Referenced in (1)

     • What a Verified System Actually Guarantees: Refinement and the Trusted Boundary

380. Irreversibility and Heat Generation in the Computing Process

     R. Landauer
     1961
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     Referenced in (1)

     • The Physical Price of Information: Erasure, Feedback and Finite-Time Thermodynamics

381. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

     Matthias Gerstgrasser; Rylan Schaeffer; Apratim Dey; et al.
     2024
     Open original source ↗
     Referenced in (1)

     • Recursive Synthetic Data and the Fate of Rare Support

382. Joint Autoregressive and Hierarchical Priors for Learned Image Compression

     David Minnen; Johannes Ballé; George Toderici
     2018
     Open original source ↗
     Referenced in (1)

     • The Bits the Model Must Actually Send: Learned Image Compression and Entropy-Model Mismatch

383. Joint Multimodal Learning with Deep Generative Models

     Masahiro Suzuki; Kotaro Nakayama; Yutaka Matsuo
     2016
     Open original source ↗
     Referenced in (1)

     • When a Sensor Is Missing: Multimodal Learning and Identifiability

384. JSON Canonicalization Scheme (JCS), RFC 8785

     Anders Rundgren; Bret Jordan; Samuel Erdtman
     2020
     Open original source ↗
     Referenced in (1)

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