Results 41 to 50 of about 12,738,743 (318)

Effective Early Stopping of Point Cloud Neural Networks [PDF]

, 2022
Early stopping techniques can be utilized to decrease the time cost, however currently the ultimate goal of early stopping techniques is closely related to the accuracy upgrade or the ability of the neural network to generalize better on unseen data ...
Zoumpekas, Thanasis, Puig, Anna, Salamó, Maria   +2 more
core   +3 more sources

Low latency low power bit flipping algorithms for LDPC decoding [PDF]

, 2010
Low Density Parity Check (LDPC) codes have been adopted in a number of wired and wireless communication standards due to their improved error correcting ability and relatively simple decoder structure.
Koçak, T, Joseph McGeehan, Taskin Kocak, Ahmed, I, Mohamed Ismail, Justin Coon, Ismail, MR, Imran Ahmed, Coon, JP, McGeehan, JP, Armour, SMD, Simon Armour   +11 more
core   +1 more source

Simplified Early Stopping Criterion for Belief-Propagation Polar Code Decoder Based on Frozen Bits

IEEE Access, 2019
Polar codes were first proposed by E. Arıkan in 2009 and have received significant attention in recent years. Successive-cancellation (SC) and belief-propagation (BP) decoding algorithms have been applied by some researchers to polar codes ...
Yongli Yan, Xuanxuan Zhang, Bin Wu
doaj   +1 more source

More Negative FRN From Stopping Searches Too Late Than Too Early: An ERP Study

Frontiers in Neuroscience, 2021
It is widely known that the feedback from a decision outcome may evoke emotions like regret, which results from a comparison between the gain the decision-maker has made and the gain he/she might make. Less is known about how search behavior is linked to
Mei Gao, Xiaolan Yang, Linanzi Zhang, Qingguo Ma, Qingguo Ma   +4 more
doaj   +1 more source

Esc: An Early-Stopping Checker for Budget-aware Index Tuning [PDF]

Proceedings of the VLDB Endowment
Index tuning is a time-consuming process. One major performance bottleneck in existing index tuning systems is the large amount of "what-if" query optimizer calls that estimate the cost of a given pair of query and index configuration without ...
Xiao-Ying Wang, Wentao Wu, Vivek R. Narasayya, Surajit Chaudhuri   +3 more
semanticscholar   +1 more source

Early Stopping is Nonparametric Variational Inference

CoRR, 2015
8 pages, 5 ...
Dougal Maclaurin, David Duvenaud, Ryan P. Adams   +2 more
openaire   +3 more sources

Optimizing Multiclass Classification Using Convolutional Neural Networks with Class Weights and Early Stopping for Imbalanced Datasets

Electronics
Multiclass classification in machine learning often faces significant challenges due to unbalanced datasets. This situation leads to biased predictions and reduced model performance.
M. N. Razali, Nureize Arbaiy, Pei-Chun Lin, Syafikrudin Ismail   +3 more
semanticscholar   +1 more source

Complexity Is an Effective Observable to Tune Early Stopping in Scenario Optimization

IEEE Transactions on Automatic Control, 2023
Scenario optimization is a broad scheme for data-driven decision-making in which experimental observations act as constraints on the feasible domain for the optimization variables. The probability with which the solution is not feasible for a new, out-of-
S. Garatti, Algo Carè, M. Campi
semanticscholar   +1 more source

Intersection of Parallels as an Early Stopping Criterion

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022
CIKM ...
Ali Vardasbi, Maarten de Rijke, Mostafa Dehghani 0001   +2 more
openaire   +4 more sources

KFold Cross-Validation and Early Stopping in Foodomics Neural Networks: Practices, Pitfalls, and Recommendations

IEEE Access
KFold Cross-Validation (CV) and Early Stopping (ES) are crucial methodologies often employed in the literature. On the one hand, KFold CV is fundamental for the robust evaluation of machine learning models, offering insights into their performance across
Dario Ruggeri, Laszlo Vidacs
doaj   +1 more source