Fuentes de referencia

Referencias 193–216 de 766

193. Denoising Diffusion Probabilistic Models

     Jonathan Ho; Ajay Jain; Pieter Abbeel
     2020
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     • Guidance Changes the Target: Conditional Diffusion Between Fidelity and Concentration

194. Dense Passage Retrieval for Open-Domain Question Answering

     Vladimir Karpukhin; Barlas Oğuz; Sewon Min; Patrick Lewis; Ledell Wu; Sergey Edunov; Danqi Chen; Wen-tau Yih
     2020
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     • Retrieval Is Part of the Model: Exposure, Evidence, and Training-Time Coupling

195. Dense Passage Retrieval for Open-Domain Question Answering

     Karpukhin et al.
     2020-11

     Ubicación en la fuente: EMNLP 2020 abstract and paper

     Alcance de la consulta: Dual encoders represent queries and passages for dense retrieval; retrieval quality and answer correctness must be measured separately.

     Consulta: 2026-10-15

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     • 检索增强生成的证据工程：召回误差、引用归因与端到端可信性

196. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

     Seth Neel; Aaron Roth; Saeed Sharifi-Malvajerdi
     2021
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     • What an Unlearning Test Can Actually Certify

197. Design Guidelines for High Performance RDMA Systems

     Anuj Kalia; Michael Kaminsky; David G. Andersen
     2016
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     • Memory across a Network: RDMA, Placement, and the Cost of Remote State

198. Design principles for enabling an anode-free sodium all-solid-state battery

     Grayson Deysher; Jin An Sam Oh; Yu-Ting Chen; Baharak Sayahpour; So-Yeon Ham; Diyi Cheng; Phillip Ridley; Ashley Cronk; Sharon Wan-Hsuan Lin; Kun Qian; Long Hoang Bao Nguyen; Jihyun Jang; Ying Shirley Meng
     2024
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     • Abundance Is Not Performance: Sodium-Ion Batteries from Electrode Metrics to Usable Packs

199. Design, construction, and in vivo augmentation of a complex gut microbiome

     Alice G. Cheng; Po-Yi Ho; Andrés Aranda-Díaz; Sunit Jain; Feiqiao B. Yu; Xiandong Meng; et al.
     2022-09

     Ubicación en la fuente: Abstract; hCom1 and hCom2 results

     Alcance de la consulta: 定义群落及无菌小鼠验证，不等同人体疗效。

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     • 从相关菌群到可控群落：微生物互作、组成数据与因果验证

200. Detecting and Correcting for Label Shift with Black Box Predictors

     Zachary C. Lipton; Yu-Xiang Wang; Alexander J. Smola
     2018
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     • Generalization Under Shift Requires Identifiable Assumptions

201. Detection and localization of surgically resectable cancers with a multi-analyte blood test

     Joshua D. Cohen; Lu Li; Yuxuan Wang; Christopher Thoburn; Bahman Afsari; Ludmila Danilova; Christopher Douville; Ammar A. Javed; Fay Wong; Austin Mattox; Ralph H. Hruban; Christopher L. Wolfgang; Michael G. Goggins; Marco Dal Molin; Tian-Li Wang; Richard Roden; Alison P. Klein; Janine Ptak; Lisa Dobbyn; Joy Schaefer; Natalie Silliman; Maria Popoli; Joshua T. Vogelstein; James D. Browne; Robert E. Schoen; Randall E. Brand; Jeanne Tie; Peter Gibbs; Hui-Li Wong; Aaron S. Mansfield; Jin Jen; Samir M. Hanash; Massimo Falconi; Peter J. Allen; Shibin Zhou; Chetan Bettegowda; Luis A. Diaz; Cristian Tomasetti; Kenneth W. Kinzler; Bert Vogelstein; Anne Marie Lennon; Nickolas Papadopoulos
     2018
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     • Finding a Rare Signal: The Statistical Architecture of Blood-Based Cancer Detection

202. Differential dataflow

     Frank McSherry; Derek G. Murray; Rebecca Isaacs; Michael Isard
     2013
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     • Maintaining a Fixed Point: Datalog, Retractions, and Incremental State

203. Differential Dataflow: The Map Operator

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

204. Differential Privacy Has Disparate Impact on Model Accuracy

     Eugene Bagdasaryan; Omid Poursaeed; Vitaly Shmatikov
     2019
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     • Clipping Changes the Gradient Before Privacy Noise Arrives

205. Differentially Private Learning with Adaptive Clipping

     Galen Andrew; Om Thakkar; H. Brendan McMahan; Swaroop Ramaswamy
     2019
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     • Clipping Changes the Gradient Before Privacy Noise Arrives

206. Diffusion Models Beat GANs on Image Synthesis

     Prafulla Dhariwal; Alex Nichol
     2021
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     • Guidance Changes the Target: Conditional Diffusion Between Fidelity and Concentration

207. Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

     , , , , , , ,
     2023-03-07

     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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     • 扩散策略与机器人闭环：多模态动作、执行时限与安全验证

208. Diffusion-LM Improves Controllable Text Generation

     Xiang Lisa Li; John Thickstun; Ishaan Gulrajani; Percy Liang; Tatsunori B. Hashimoto
     2022
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     • What a Mask Forgets: Discrete Diffusion, Joint Dependence, and Parallel Token Generation

209. DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor With Application to In-Hand Manipulation

     Mike Lambeta; Po-Wei Chou; Stephen Tian; Brian Yang; Benjamin Maloon; Victoria Rose Most; Dave Stroud; Raymond Santos; Ahmad Byagowi; Gregg Kammerer; Dinesh Jayaraman; Roberto Calandra
     2020
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     • Touch as an Active Measurement: Tactile Robotics, Contact Ambiguity and Robust Manipulation

210. Direct Preference Optimization: Your Language Model is Secretly a Reward Model

     Rafael Rafailov; Archit Sharma; Eric Mitchell; Stefano Ermon; Christopher D. Manning; Chelsea Finn
     2023
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     • Preference Optimization Does Not Identify Human Utility

211. Direct Preference Optimization: Your Language Model is Secretly a Reward Model

     Rafailov et al.
     2023-05-29

     Ubicación en la fuente: v3; sections 3-5

     Alcance de la consulta: A policy/reward reparameterization turns the stated KL-constrained preference objective into a direct classification-style loss under its assumptions.

     Consulta: 2026-10-15

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     • 偏好优化的目标边界：奖励代理、参考策略与分布漂移

212. Direction-Optimizing Breadth-First Search

     Scott Beamer; Krste Asanović; David Patterson
     2012
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     • Skew Is the Workload: Partitioning and Direction Choice in Graph Processing

213. Discovering governing equations from data by sparse identification of nonlinear dynamical systems

     Steven L. Brunton; Joshua L. Proctor; J. Nathan Kutz
     2016
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     • Discovering an Equation from Data: Sparse Structure, Hidden Variables and Experimental Design

214. Discovery of a structural class of antibiotics with explainable deep learning

     Wong et al.
     2023-12-20

     Ubicación en la fuente: 39,312 measured compounds; 12,076,365 predicted; 283 tested; preclinical activity and structural rationales.

     Consulta: 2026-10-22

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     • AI 抗生素发现的证据链：化学空间、可解释结构与临床前验证

215. Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

     Aaron Lou; Chenlin Meng; Stefano Ermon
     2024
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     • What a Mask Forgets: Discrete Diffusion, Joint Dependence, and Parallel Token Generation

216. Disease variant prediction with deep generative models of evolutionary data

     Jonathan Frazer; Pascal Notin; Mafalda Dias; Aidan Gomez; Joseph K. Min; Kelly Brock; Yarin Gal; Debora S. Marks
     2021
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     • Predicting a Molecular Consequence: Variant-Effect Models between Evolution and Clinical Evidence