参考文献

全766件中 721–744件

721. Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

     ,
     2024-05-31

     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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     • 选择性状态空间模型：Mamba 的递推、扫描与记忆边界

722. Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

     Tri Dao; Albert Gu
     2024
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     • What a Recurrent State Can Remember: Decay, Selectivity, and Sequence Memory

723. Transformers learn in-context by gradient descent

     Johannes von Oswald; Eyvind Niklasson; Ettore Randazzo; João Sacramento; Alexander Mordvintsev; Andrey Zhmoginov; Max Vladymyrov
     2022
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     • Learning Inside the Context: Bayesian Ambiguity and Algorithmic Imitation

724. Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration

     Sanne E. Klompe; Phuc L. H. Vo; Tyler S. Halpin-Healy; Samuel H. Sternberg
     2019-06-12

     参照箇所: Abstract; integration-site distribution

     確認範囲: 另一类RNA引导整合系统；整合位置与完整性需单独评价。

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     • 可编程大片段整合：RNA引导转座的定位、完整性与基因组代价

725. Triptycene Induced Enhancement of Membrane Gas Selectivity for Microporous Tröger's Base Polymers

     Mariolino Carta; Matthew Croad; Richard Malpass‐Evans; Johannes C. Jansen; Paola Bernardo; Gabriele Clarizia; Karel Friess; Marek Lanč; Neil B. McKeown
     2014
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     • Separating Molecules at Scale: Membrane Selectivity, Permeance and Process-Level Value

726. Tutorial: Introduction to Datalog

     Souffle project
     2026
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     • Maintaining a Fixed Point: Datalog, Retractions, and Incremental State

727. Ultrabroadband and band-selective thermal meta-emitters by machine learning

     Xiao et al.
     2025-07-02

     参照箇所: Machine-learning-guided three-dimensional photonic material design; optical and fabrication validation.

     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

728. Ultralow thermal conductivity and high thermoelectric figure of merit in SnSe crystals

     Li-Dong Zhao; Shih-Han Lo; Yongsheng Zhang; Hui Sun; Gangjian Tan; Ctirad Uher; C. Wolverton; Vinayak P. Dravid; Mercouri G. Kanatzidis
     2014
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     • From a Large Figure of Merit to Useful Power: Thermoelectric Materials under Real Constraints

729. Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

     Perreault Levasseur, Hezaveh and Wechsler
     2017-08-29

     参照箇所: Variational uncertainty, coverage calibration and assumed lens family.

     確認日: 2026-10-22

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     • AI 引力透镜反演：快速参数推断、质量片退化与宇宙学选择效应

730. Understanding and mitigating gradient pathologies in physics-informed neural networks

     Sifan Wang; Yujun Teng; Paris Perdikaris
     2020
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     • Physics-Informed Learning Has a Scale: Residual Weighting, Units, and Error

731. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

     Tongzhou Wang; Phillip Isola
     2020
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     • Contrastive Learning Between Alignment and Information Loss

732. Understanding Gradient Clipping in Private SGD: A Geometric Perspective

     Xiangyi Chen; Zhiwei Steven Wu; Mingyi Hong
     2020
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     • Clipping Changes the Gradient Before Privacy Noise Arrives

733. Uniform Resource Identifier (URI): Generic Syntax (RFC 3986)

     Tim Berners-Lee; Roy T. Fielding; Larry Masinter
     2005
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     • One Message, Two Languages: Parser Differentials as a Security Boundary

734. URL Standard

     WHATWG
     2026
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     • One Message, Two Languages: Parser Differentials as a Security Boundary

735. Using EXPLAIN

     PostgreSQL Global Development Group
     2026
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     • Query Plans under Uncertainty: Cardinality Error, Regret, and Robust Execution

736. Variational Autoencoders and Nonlinear ICA: A Unifying Framework

     Ilyes Khemakhem; Diederik P. Kingma; Ricardo Pio Monti; Aapo Hyvärinen
     2019
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     • Interventions Do Not Name Latents: Causal Representation and Identifiability

737. Variational Image Compression with a Scale Hyperprior

     Johannes Ballé; David Minnen; Saurabh Singh; Sung Jin Hwang; Nick Johnston
     2018
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     • The Bits the Model Must Actually Send: Learned Image Compression and Entropy-Model Mismatch

738. Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

     Yuge Shi; N. Siddharth; Brooks Paige; Philip H. S. Torr
     2019
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     • When a Sensor Is Missing: Multimodal Learning and Identifiability

739. Verifying Solutions to Semantics-Guided Synthesis Problems

     Charlie Murphy; Keith Johnson; Thomas Reps; Loris D'Antoni
     2025
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     • Programs from Counterexamples: Synthesis, Specifications, and Oracle Limits

740. Visualizing the Loss Landscape of Neural Nets

     Hao Li; Zheng Xu; Gavin Taylor; Christoph Studer; Tom Goldstein
     2018
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     • The Same Predictor, a Sharper Minimum: Parameter Symmetry and the Meaning of Loss Geometry

741. Water harvesting from air with metal-organic frameworks powered by natural sunlight

     Hyunho Kim; Sungwoo Yang; Sameer R. Rao; Shankar Narayanan; Eugene A. Kapustin; Hiroyasu Furukawa; Ari S. Umans; Omar M. Yaghi; Evelyn N. Wang
     2017
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     • Harvesting Water from Dry Air: Sorption Thermodynamics, Cycling and Delivered Yield

742. Watermarks in Stream Processing Systems: Semantics and Comparative Analysis of Apache Flink and Google Cloud Dataflow

     Tyler Akidau; Edmon Begoli; Slava Chernyak; Fabian Hueske; Kathryn Knight; Kenneth Knowles; Daniel Mills; Dan Sotolongo
     2021
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     • When a Stream Is Complete: Event Time, Watermarks, and Revisable Results

743. Weakly-Supervised Disentanglement Without Compromises

     Francesco Locatello; Ben Poole; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem; Michael Tschannen
     2020
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     • Interventions Do Not Name Latents: Causal Representation and Identifiability

744. WeatherBench 2: A benchmark for the next generation of data-driven global weather models

     Rasp et al.
     2023-08-29

     参照箇所: Primary benchmark specification: initialization, regridding, reference analyses and multiple diagnostic metrics.

     確認日: 2026-10-22

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     • 从天气技巧到气候可信性：可微分动力核心与学习参数化的稳定性
     • 天气预测从一条轨迹到概率分布：GenCast 的校准、联合风险与极端事件