参考文献

全766件中 49–72件

49. Adaptive Computation Time for Recurrent Neural Networks

    Alex Graves
    2016
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    • The Price of Stopping Early: Adaptive Depth, Confidence, and the Systems Cost of Neural Decisions

50. Advancing regulatory variant effect prediction with AlphaGenome

    Avsec et al.
    2026-01-28

    参照箇所: Abstract; sequence-to-track prediction, 11 modalities and variant-effect benchmark scope.

    確認日: 2026-10-22

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    • 读懂非编码基因组：长程调控模型的尺度、变异效应与因果识别

51. AIFM: High-Performance, Application-Integrated Far Memory

    Zhenyuan Ruan; Malte Schwarzkopf; Marcos K. Aguilera; Adam Belay
    2020
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    • Memory across a Network: RDMA, Placement, and the Cost of Remote State

52. ALEX: An Updatable Adaptive Learned Index

    Jialin Ding; Umar Farooq Minhas; Jia Yu; Chi Wang; Jaeyoung Do; Yinan Li; Hantian Zhang; Badrish Chandramouli; Johannes Gehrke; Donald Kossmann; David Lomet; Tim Kraska
    2020
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    • Learned Indexes Under Distribution Change: Prediction, Search, and Correctness

53. Alive2: bounded translation validation for LLVM

    Nuno P. Lopes; Juneyoung Lee; Chung-Kil Hur; Zhengyang Liu; John Regehr
    2021
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    • Optimization as Refinement: Proving What a Compiler May Change

54. All File Systems Are Not Created Equal: On the Complexity of Crafting Crash-Consistent Applications

    Thanumalayan Sankaranarayana Pillai; Vijay Chidambaram; Ramnatthan Alagappan; Samer Al-Kiswany; Andrea C. Arpaci-Dusseau; Remzi H. Arpaci-Dusseau
    2014
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    • After the Power Fails: Crash Consistency and the Meaning of Durable

55. An absorption profile centred at 78 megahertz in the sky-averaged spectrum

    Judd D. Bowman; Alan E. E. Rogers; Raul A. Monsalve; Thomas J. Mozdzen; Nivedita Mahesh
    2018
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    • Listening beneath the Foreground: Calibration and Identifiability in Cosmic-Dawn Radio Experiments

56. An autonomous laboratory for the accelerated synthesis of inorganic materials

    Szymanski et al.
    2023

    参照箇所: Robotic solid-state workflow; phase identification, Rietveld refinement and active-learning feedback.

    確認日: 2026-10-22

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    • 自主实验室的科学闭环：主动学习、测量误判与可追溯发现

57. An Empirical Model of Large-Batch Training

    Sam McCandlish; Jared Kaplan; Dario Amodei; OpenAI Dota Team
    2018
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    • What Gradient Noise Actually Controls

58. An epigenetic biomarker of aging for lifespan and healthspan

    Morgan E. Levine; Ake T. Lu; Austin Quach; Brian H. Chen; Themistocles L. Assimes; Stefania Bandinelli; Lifang Hou; Andrea A. Baccarelli; James D. Stewart; Yun Li; Eric A. Whitsel; James G Wilson; Alex P Reiner; Abraham Aviv; Kurt Lohman; Yongmei Liu; Luigi Ferrucci; Steve Horvath
    2018
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    • What Does an Epigenetic Clock Measure? Prediction, Intervention and the Problem of Biological Age

59. An Essential Role for Senescent Cells in Optimal Wound Healing through Secretion of PDGF-AA

    Marco Demaria; Naoko Ohtani; Sameh A. Youssef; Francis Rodier; Wendy Toussaint; James R. Mitchell; Remi-Martin Laberge; Jan Vijg; Harry Van Steeg; Martijn E.T. Dollé; Jan H.J. Hoeijmakers; Alain de Bruin; Eiji Hara; Judith Campisi
    2014
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    • The Context of Cellular Senescence: Tissue Repair, Persistent Damage and Selective Intervention

60. An Explanation of In-context Learning as Implicit Bayesian Inference

    Sang Michael Xie; Aditi Raghunathan; Percy Liang; Tengyu Ma
    2021
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    • Learning Inside the Context: Bayesian Ambiguity and Algorithmic Imitation

61. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Alexey Dosovitskiy; Lucas Beyer; Alexander Kolesnikov; Dirk Weissenborn; Xiaohua Zhai; Thomas Unterthiner; Mostafa Dehghani; Matthias Minderer; Georg Heigold; Sylvain Gelly; Jakob Uszkoreit; Neil Houlsby
    2021
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    • Where Vision Transformers Put Their Inductive Bias

62. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Dosovitskiy et al.
    2020-10-22

    参照箇所: v2; architecture and scaling experiments

    確認範囲: Images split into patches become tokens for a Transformer; large-scale pretraining matters to results.

    確認日: 2026-10-15

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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

63. An Image is Worth 32 Tokens for Reconstruction and Generation

    Qihang Yu; Mark Weber; Xueqing Deng; Xiaohui Shen; Daniel Cremers; Liang-Chieh Chen
    2024
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    • Before the Generator Learns: Visual Tokenization, Information Bottlenecks, and Sequence Cost

64. An improved data stream summary: the count-min sketch and its applications

    Graham Cormode; S. Muthukrishnan
    2005
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    • Approximation with a Contract: Error, Randomness, and Mergeability in Streaming Sketches

65. An integrated transcriptomic cell atlas of human neural organoids

    Zhisong He; Leander Dony; Jonas Simon Fleck; Artur Szałata; Katelyn X. Li; Irena Slišković; et al.
    2024-11-20

    参照箇所: Abstract; atlas integration and protocol comparisons

    確認範囲: 整合36数据集/26协议；细胞级样本数不替代供体与批次。

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    • 类器官怎样成为可靠模型：细胞组成、供体差异与发育可比性

66. Ancient Admixture in Human History

    Nick Patterson; Priya Moorjani; Yontao Luo; Swapan Mallick; Nadin Rohland; Yiping Zhan; et al.
    2012-11-01

    参照箇所: f-statistics methods and admixture model tests

    確認範囲: 群体历史统计模型，不把祖源统计等同文化或个体身份。

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    • 古基因组如何重建迁徙：祖源模型、时间采样与历史解释的边界

67. ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

    Amir Gholami; Kurt Keutzer; George Biros
    2019
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    • The Solver Is Part of the Network: Neural ODEs, Discrete Gradients, and Numerical Fidelity

68. ARC: A Self-Tuning, Low Overhead Replacement Cache

    Nimrod Megiddo; Dharmendra S. Modha
    2003
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    • Caching Beyond Recency: Admission, Workload Shift, and the Cost of a Miss

69. Are Transformers Effective for Time Series Forecasting?

    Ailing Zeng; Muxi Chen; Lei Zhang; Qiang Xu
    2022
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    • Forecasts Must Respect the Clock: Temporal Leakage and Evaluation

70. Are Transformers universal approximators of sequence-to-sequence functions?

    Chulhee Yun; Srinadh Bhojanapalli; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar
    2019
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    • Rank Is Not a Capacity Certificate: Attention Bottlenecks Beyond the Score Matrix

71. Assembly of functionally integrated human forebrain spheroids

    Fikri Birey; Jimena Andersen; Christopher D. Makinson; Saiful Islam; Wu Wei; Nina Huber; et al.
    2017-04-26

    参照箇所: Abstract; Figs. 2–4

    確認範囲: 特定前脑类器官迁移与整合；不等同完整人脑或意识。

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    • 类器官怎样成为可靠模型：细胞组成、供体差异与发育可比性

72. Assessment of methane emissions from the U.S. oil and gas supply chain

    Ramón A. Alvarez; Daniel Zavala-Araiza; David R. Lyon; David T. Allen; Zachary R. Barkley; Adam R. Brandt; Kenneth J. Davis; Scott C. Herndon; Daniel J. Jacob; Anna Karion; Eric A. Kort; Brian K. Lamb; Thomas Lauvaux; Joannes D. Maasakkers; Anthony J. Marchese; Mark Omara; Stephen W. Pacala; Jeff Peischl; Allen L. Robinson; Paul B. Shepson; Colm Sweeney; Amy Townsend-Small; Steven C. Wofsy; Steven P. Hamburg
    2018
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    • Closing the Methane Budget: Atmospheric Inversions, Intermittent Sources and Independent Validation