AUTURNAL / RESEARCH SOURCES

المصادر المرجعية

المراجع الأولية المستخدمة في المجموعة. تحتفظ العناوين والبيانات الببليوغرافية بلغتها الأصلية للتحقق الدقيق.

المراجع 97–120 من 766

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull; Miquel Perello-Nieto; Markus Kängsepp; Telmo Silva Filho; Hao Song; Peter Flach
2019
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Calibrated Probabilities Are Not Yet Good Decisions
Billion-scale similarity search with GPUs
Jeff Johnson; Matthijs Douze; Hervé Jégou
2017
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Searching with an Error Budget: Approximate Neighbors, Recall, and Workload Geometry
BirdNET: A deep learning solution for avian diversity monitoring
Kahl, Wood, Eibl and Klinck
2021
الموضع في المصدر: Original 984-species model; single-species recordings and annotated soundscapes are different evaluation domains.
تاريخ الاطلاع: 2026-10-22
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- 听见不等于存在多少：生态声学 AI、检测概率与生物多样性推断
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill; Florian Strub; Florent Altché; Corentin Tallec; Pierre H. Richemond; Elena Buchatskaya; Carl Doersch; Bernardo Avila Pires; Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Koray Kavukcuoglu; Rémi Munos; Michal Valko
2020
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Learning from a Moving Teacher: Self-Distillation, Temporal Smoothing, and Collapse Boundaries
Born Again Neural Networks
Tommaso Furlanello; Zachary C. Lipton; Michael Tschannen; Laurent Itti; Anima Anandkumar
2018
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Learning from a Moving Teacher: Self-Distillation, Temporal Smoothing, and Collapse Boundaries
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Surat Teerapittayanon; Bradley McDanel; H.T. Kung
2017
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- The Price of Stopping Early: Adaptive Depth, Confidence, and the Systems Cost of Neural Decisions
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Linting Xue; Aditya Barua; Noah Constant; Rami Al-Rfou; Sharan Narang; Mihir Kale; Adam Roberts; Colin Raffel
2021
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Tokens Are Not Units of Meaning: Probability Accounting Across Segmentations
Byte Latent Transformer: Patches Scale Better Than Tokens
Artidoro Pagnoni; Ram Pasunuru; Pedro Rodriguez; John Nguyen; Benjamin Muller; Margaret Li; Chunting Zhou; Lili Yu; Jason Weston; Luke Zettlemoyer; Gargi Ghosh; Mike Lewis; Ari Holtzman; Srinivasan Iyer
2024
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Tokens Are Not Units of Meaning: Probability Accounting Across Segmentations
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork; Frank McSherry; Kobbi Nissim; Adam Smith
2006
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Privacy Budgets Under Composition: Accounting for Repeated Statistical Release
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia; Emily Fertig; Jie Ren; Zachary Nado; D Sculley; Sebastian Nowozin; Joshua V. Dillon; Balaji Lakshminarayanan; Jasper Snoek
2019
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- When Many Models Share One Mistake: Error Correlation and Epistemic Signals in Deep Ensembles
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Jonathan H. Clark; Dan Garrette; Iulia Turc; John Wieting
2021
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Tokens Are Not Units of Meaning: Probability Accounting Across Segmentations
Canopy: An End-to-End Performance Tracing And Analysis System
Jonathan Kaldor; Jonathan Mace; Michał Bejda; Edison Gao; Wiktor Kuropatwa; Joe O'Neill; Kian Win Ong; Bill Schaller; Pingjia Shan; Brendan Viscomi; Vinod Venkataraman; Kaushik Veeraraghavan; Yee Jiun Song
2017
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Seeing a Distributed Execution: Sampling, Context, and the Limits of Trace Evidence
Capability Hardware Enhanced RISC Instructions: CHERI Instruction-Set Architecture (Version 9)
Robert N. M. Watson; Peter G. Neumann; Jonathan Woodruff; Michael Roe; Hesham Almatary; Jonathan Anderson; John Baldwin; Graeme Barnes; David Chisnall; Jessica Clarke; Brooks Davis; Lee Eisen; Nathaniel Wesley Filardo; Franz A. Fuchs; Richard Grisenthwaite; Alexandre Joannou; Ben Laurie; A. Theodore Markettos; Simon W. Moore; Steven J. Murdoch; Kyndylan Nienhuis; Robert Norton; Alexander Richardson; Peter Rugg; Peter Sewell; Stacey Son; Hongyan Xia
2023
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Memory Safety with Capabilities: Spatial Authority, Lifetime, and Revocation
Carbon dioxide capture from open air using covalent organic frameworks
Zihui Zhou; Tianqiong Ma; Heyang Zhang; Saumil Chheda; Haozhe Li; Kaiyu Wang; et al.
2024-10-23
الموضع في المصدر: Abstract; Figs. 3–4; updated article
نطاق التحقق: COF-999材料结果；已检查原网页提示2024-12-04 Author Correction，引用更新页，不把吸附量等同净移除。
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- 从吸附材料到净碳移除：直接空气捕集的热力学与生命周期边界
Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
Bilge Acun; Benjamin Lee; Fiodar Kazhamiaka; Kiwan Maeng; Manoj Chakkaravarthy; Udit Gupta; David Brooks; Carole-Jean Wu
2023
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- When Computation Should Run: Carbon-Aware Scheduling under Deadlines and Forecast Error
Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees
Sophie Hall; Francesco Micheli; Giuseppe Belgioioso; Ana Radovanović; Florian Dörfler
2025
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- When Computation Should Run: Carbon-Aware Scheduling under Deadlines and Forecast Error
Carbon-Aware Computing for Datacenters
Ana Radovanovic; Ross Koningstein; Ian Schneider; Bokan Chen; Alexandre Duarte; Binz Roy; Diyue Xiao; Maya Haridasan; Patrick Hung; Nick Care; Saurav Talukdar; Eric Mullen; Kendal Smith; MariEllen Cottman; Walfredo Cirne
2021
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- When Computation Should Run: Carbon-Aware Scheduling under Deadlines and Forecast Error
Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes
Darren H. S. Tan; Yu-Ting Chen; Hedi Yang; Wurigumula Bao; Bhagath Sreenarayanan; Jean-Marie Doux; et al.
2021-09-24
الموضع في المصدر: Abstract; interface and cycling results
نطاق التحقق: 微硅/硫化物体系；压力、电极负载与整电池口径需分别报告。
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- 固态电池的界面现实：离子传输、电子泄漏与整电池能量
Certified Adversarial Robustness via Randomized Smoothing
Jeremy Cohen; Elan Rosenfeld; J. Zico Kolter
2019
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Robustness Claims Need an Explicit Threat Model
Certified Data Removal from Machine Learning Models
Chuan Guo; Tom Goldstein; Awni Hannun; Laurens van der Maaten
2020
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- What an Unlearning Test Can Actually Certify
CertiKOS: An Extensible Architecture for Building Certified Concurrent OS Kernels
Ronghui Gu; Zhong Shao; Hao Chen; Xiongnan Wu; Jieung Kim; Vilhelm Sjoberg; David Costanzo
2016
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- What a Verified System Actually Guarantees: Refinement and the Trusted Boundary
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei et al.
2022-01-28
الموضع في المصدر: v6; method and limitations
نطاق التحقق: Intermediate-step exemplars improved studied arithmetic, symbolic and commonsense reasoning tasks; generated explanation is not guaranteed correct.
تاريخ الاطلاع: 2026-10-15
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- 语言模型推理的验证边界：采样、搜索与正确性证据
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem
2019
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- What an Interpretable Feature Actually Establishes
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem
2018
فتح المصدر الأصلي ↗
مراجعات تستشهد بهذا المصدر (1)
- Interventions Do Not Name Latents: Causal Representation and Identifiability

المصادر المرجعية

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

Billion-scale similarity search with GPUs

BirdNET: A deep learning solution for avian diversity monitoring

Bootstrap your own latent: A new approach to self-supervised Learning

Born Again Neural Networks

BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks

ByT5: Towards a token-free future with pre-trained byte-to-byte models

Byte Latent Transformer: Patches Scale Better Than Tokens

Calibrating Noise to Sensitivity in Private Data Analysis

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

Canopy: An End-to-End Performance Tracing And Analysis System

Capability Hardware Enhanced RISC Instructions: CHERI Instruction-Set Architecture (Version 9)

Carbon dioxide capture from open air using covalent organic frameworks

Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters

Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees

Carbon-Aware Computing for Datacenters

Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes

Certified Adversarial Robustness via Randomized Smoothing

Certified Data Removal from Machine Learning Models

CertiKOS: An Extensible Architecture for Building Certified Concurrent OS Kernels

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations