BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Daube C, Xu T, Zhan J, Webb A, Ince RAA, Garrod OGB, Schyns PG. Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity. Patterns (N Y) 2021;2:100348. [PMID: 34693374 DOI: 10.1016/j.patter.2021.100348] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Schyns PG, Snoek L, Daube C. Degrees of algorithmic equivalence between the brain and its DNN models. Trends Cogn Sci 2022;26:1090-102. [PMID: 36216674 DOI: 10.1016/j.tics.2022.09.003] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
2 Compton A, Roop BW, Parrell B, Lammert AC. Stimulus whitening improves the efficiency of reverse correlation. Behav Res Methods 2022. [PMID: 36038814 DOI: 10.3758/s13428-022-01946-w] [Reference Citation Analysis]
3 Jozwik KM, O'Keeffe J, Storrs KR, Guo W, Golan T, Kriegeskorte N. Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models. Proc Natl Acad Sci U S A 2022;119:e2115047119. [PMID: 35767642 DOI: 10.1073/pnas.2115047119] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Yan Y, Zhan J, Ince RA, Schyns PG. Network predictions sharpen the representation of visual features for categorization.. [DOI: 10.1101/2022.07.01.498431] [Reference Citation Analysis]
5 Ong AKS, Prasetyo YT, Yuduang N, Nadlifatin R, Persada SF, Robas KPE, Chuenyindee T, Buaphiban T. Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand. IJERPH 2022;19:7979. [DOI: 10.3390/ijerph19137979] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
6 Ong AKS, Chuenyindee T, Prasetyo YT, Nadlifatin R, Persada SF, Gumasing MJJ, German JD, Robas KPE, Young MN, Sittiwatethanasiri T. Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand "ThaiChana". Int J Environ Res Public Health 2022;19:6111. [PMID: 35627647 DOI: 10.3390/ijerph19106111] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
7 Fujita T, Terayama K, Sumita M, Tamura R, Nakamura Y, Naito M, Tsuda K. Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring. Science and Technology of Advanced Materials. [DOI: 10.1080/14686996.2022.2075240] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Jiahui G, Feilong M, Oleggio Castello MVD, Nastase SA, Haxby JV, Gobbini MI. Modeling naturalistic face processing in humans with deep convolutional neural networks.. [DOI: 10.1101/2021.11.17.469009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
9 Jozwik KM, O’keeffe J, Storrs KR, Guo W, Golan T, Kriegeskorte N. Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models.. [DOI: 10.1101/2021.04.09.438859] [Reference Citation Analysis]