Results 11 to 20 of about 12,738,743 (318)

Low Complexity Early Stopping Belief Propagation Decoder for Polar Codes

IEEE Access
Belief propagation is one of low latency decoding algorithms for polar codes but it requires relatively high decoding complexity due to its inherent iterative decoding nature.
Chungsu Lee, Chansoo Park, Sungyeol Back, Wangrok Oh   +3 more
doaj   +4 more sources

A Recursive Early-Stopping Phase King Protocol

ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, 2022
Early-stopping consensus protocols guarantee termination within a number of rounds that depends only on the actual number f of faulty nodes in a run, not the maximum number of faults that can be tolerated.
C. Lenzen, Sahar Sheikholeslami
semanticscholar   +3 more sources

Early Stopping Tabular In-Context Learning

CoRR
Tabular foundation models have shown strong performance across various tabular learning tasks via in-context learning, offering robust generalization without any downstream finetuning.
Jaris Küken, L. Purucker, Frank Hutter
semanticscholar   +3 more sources

Noisy Early Stopping for Noisy Labels [PDF]

CoRR
Training neural network classifiers on datasets contaminated with noisy labels significantly increases the risk of overfitting. Thus, effectively implementing Early Stopping in noisy label environments is crucial. Under ideal circumstances, Early Stopping utilises a validation set uncorrupted by label noise to effectively monitor generalisation during ...
William Toner, Amos J. Storkey
core   +4 more sources

Rethinking Early Stopping: Refine, Then Calibrate

CoRR
Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains.
E. Berta, David Holzmüller, Michael I. Jordan, Francis R. Bach   +3 more
semanticscholar   +4 more sources

Early stopping strategies in Deep Image Prior [PDF]

Soft Computing - A Fusion of Foundations, Methodologies and Applications
The so-called Deep Image Prior approach is an unsupervised deep learning methodology which has gained great interest in recent years due to its effectiveness in tackling imaging problems. However, a well known drawback of Deep Image Prior is the need for
A. Benfenati, Ambra Catozzi, Giorgia Franchini, Federica Porta   +3 more
semanticscholar   +2 more sources

Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or redundant reasoning -- so-called overthinking -- can increase inference costs
Ren-Liang Sun, Wei Cheng, Dawei Li, Hai-Feng Chen, Wei Wang   +4 more
semanticscholar   +4 more sources

Early stopping for conjugate gradients in statistical inverse problems [PDF]

Numerische Mathematik
We consider estimators obtained by iterates of the conjugate gradient (CG) algorithm applied to the normal equation of prototypical statistical inverse problems.
Laura Hucker, Markus Reiss
semanticscholar   +3 more sources

Clinical equipoise and ethical challenges in expanding endovascular stroke thrombectomy practice [PDF]

Frontiers in Surgery
IntroductionEndovascular thrombectomy transformed acute ischemic stroke care while exposing ethical tensions in emergency research. As landmark trials rapidly shifted standards, investigators faced complex questions regarding equipoise, early stopping ...
Aryan Wadhwa, Evan P. McNeil, Pratham B. Bhatt, Kimberly Han, Shashvat Purohit, Justin Granstein, Christopher S. Ogilvy, Mario Ganau, Philipp Taussky   +8 more
doaj   +2 more sources

Early Stopping of Untrained Convolutional Neural Networks

SIAM Journal on Imaging Sciences
In recent years, new regularization methods based on (deep) neural networks have shown very promising empirical performance for the numerical solution of ill-posed problems, e.g., in medical imaging and imaging science.
T. Jahn, Bang-Ti Jin
semanticscholar   +4 more sources