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研究集で引用した一次資料です。照合しやすいよう、題名と書誌情報は原文を保持しています。

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Improved Techniques for Training Consistency Models
Yang Song; Prafulla Dhariwal
2023
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- One Step Is a Budget, Not a Distribution: Consistency Models and the Evidence for Fast Generation
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
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- Retrieval Is Part of the Model: Exposure, Evidence, and Training-Time Coupling
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle; Yu-Xiang Wang
2018
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- Privacy Budgets Under Composition: Accounting for Repeated Statistical Release
In Search of an Understandable Consensus Algorithm
Diego Ongaro; John Ousterhout
2014
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- Consensus Beyond the Happy Path: Safety, Progress, and the Cost of Quorums
In Search of Lost Domain Generalization
Ishaan Gulrajani; David Lopez-Paz
2020
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- Auditing the Shortcut: Feature Interventions, Hidden Groups, and the Limits of Robustness Claims
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
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- Learning Inside the Context: Bayesian Ambiguity and Algorithmic Imitation
in-toto: Providing farm-to-table guarantees for bits and bytes
Santiago Torres-Arias; Hammad Afzali; Trishank Karthik Kuppusamy; Reza Curtmola; Justin Cappos
2019
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- From Source to Artifact: Reproducible Builds and the Limits of Provenance
Inductive Representation Learning on Large Graphs
, ,
2017-06-07
参照箇所: Abstract; 原文方法、理论和实验章节见作者原始论文链接
確認範囲: 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。
確認日: 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
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
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- Forecasts Must Respect the Clock: Temporal Leakage and Evaluation
Intel SGX and TDX DCAP Quote Verification Service
Intel
2026
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- What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary
Intel SGX Explained
Victor Costan; Srinivas Devadas
2016
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- What an Attestation Establishes: Confidential Computing and the Remaining Trust Boundary
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
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- The Tumor Is Not a Sample: Spatial Evolution, Detection Limits and Longitudinal Inference
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
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- Recognition Is Only the Beginning: Engineering Cell Therapies across Solid-Tumor Barriers
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
Armen Aghajanyan; Luke Zettlemoyer; Sonal Gupta
2020
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- What Low-Rank Adaptation Can and Cannot Represent
Invariant Risk Minimization
, , ,
2019-07-05
参照箇所: Abstract; 原文方法、理论和实验章节见作者原始论文链接
確認範囲: 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。
確認日: 2026-10-21
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- 从相关到干预：AI 因果推断中的识别、混杂与可迁移性
Invariant Risk Minimization
Martin Arjovsky; Léon Bottou; Ishaan Gulrajani; David Lopez-Paz
2019
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- Generalization Under Shift Requires Identifiable Assumptions
Invariant Risk Minimization
Martin Arjovsky; Léon Bottou; Ishaan Gulrajani; David Lopez-Paz
2019
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- Auditing the Shortcut: Feature Interventions, Hidden Groups, and the Limits of Robustness Claims
Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing
Sved et al.
2026-03-04
参照箇所: Three-dimensional FDTD design and measured compact optical classifiers; density does not equal general-purpose throughput.
確認日: 2026-10-22
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- 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
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
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- What a Verified System Actually Guarantees: Refinement and the Trusted Boundary
Irreversibility and Heat Generation in the Computing Process
R. Landauer
1961
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- The Physical Price of Information: Erasure, Feedback and Finite-Time Thermodynamics
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser; Rylan Schaeffer; Apratim Dey; et al.
2024
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- Recursive Synthetic Data and the Fate of Rare Support
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen; Johannes Ballé; George Toderici
2018
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- The Bits the Model Must Actually Send: Learned Image Compression and Entropy-Model Mismatch
Joint Multimodal Learning with Deep Generative Models
Masahiro Suzuki; Kotaro Nakayama; Yutaka Matsuo
2016
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- When a Sensor Is Missing: Multimodal Learning and Identifiability
JSON Canonicalization Scheme (JCS), RFC 8785
Anders Rundgren; Bret Jordan; Samuel Erdtman
2020
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- One Message, Two Languages: Parser Differentials as a Security Boundary

参考文献

Improved Techniques for Training Consistency Models

Improving language models by retrieving from trillions of tokens

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

In Search of an Understandable Consensus Algorithm

In Search of Lost Domain Generalization

In-context Learning and Induction Heads

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

Inductive Representation Learning on Large Graphs

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

Intel SGX and TDX DCAP Quote Verification Service

Intel SGX Explained

Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Intraventricular CARv3-TEAM-E T Cells in Recurrent Glioblastoma

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Invariant Risk Minimization

Invariant Risk Minimization

Invariant Risk Minimization

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

IronFleet: proving practical distributed systems correct

Irreversibility and Heat Generation in the Computing Process

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

Joint Autoregressive and Hierarchical Priors for Learned Image Compression

Joint Multimodal Learning with Deep Generative Models

JSON Canonicalization Scheme (JCS), RFC 8785