Advances in neural information processing systems 7

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906

Probabilistic topic models

Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516

Machine learning: Algorithms, real-world applications and research directions

Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312

Hierarchical models of object recognition in cortex

Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309

A survey of the recent architectures of deep convolutional neural networks

Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137

Generalizing from a Few Examples: A Survey on Few-shot Learning

Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165

Deep learning applications and challenges in big data analytics

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594

Datasheets for Datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474

Understanding deep learning (still) requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM · doi:10.1145/3446776 · Cited by 2,400

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370

Datasheets for datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469

A Survey on Large Language Model based Autonomous Agents

Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.11432 · Cited by 1,925

Graph convolutional networks: a comprehensive review

Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888

A survey on federated learning

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899

Natural language processing: state of the art, current trends and challenges

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13428-4 · Cited by 1,823

Scientific discovery in the age of artificial intelligence.

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823

Can we open the black box of AI?

Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773

A survey on large language model based autonomous agents

Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40231-1 · Cited by 1,941

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458

Graph based anomaly detection and description: a survey

Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560

Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training

Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_18 · Cited by 1,464

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328

Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach

Norman, K. A., & O'Reilly, R. C. (2003) · Psychological Review · doi:10.1037/0033-295x.110.4.611 · Cited by 1,312

Transforming classifier scores into accurate multiclass probability estimates

Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Boughorbel, S., Jarray, F., & El-Anbari, M. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177678 · Cited by 1,222

Parameter-efficient fine-tuning of large-scale pre-trained language models

Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191

Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios

Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01925-4 · Cited by 1,120

Deep learning for brain MRI segmentation: state of the art and future directions

Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129

Untitled work

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Deep learning modelling techniques: Current progress, applications, advantages, and challenges

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069

Acquisition of Localization Confidence for Accurate Object Detection

Jiang, B., Luo, R., Mao, J., Xiao, T., & Jiang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_48 · Cited by 1,059

MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification

Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., & Ni, B. (2023) · Scientific Data · doi:10.1038/s41597-022-01721-8 · Cited by 1,043

A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities

Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036

A Connection Between Score Matching and Denoising Autoencoders

Vincent, P. (2011) · Neural Computation · doi:10.1162/neco_a_00142 · Cited by 1,011

A feedforward architecture accounts for rapid categorization

Serre, T., Oliva, A., & Poggio, T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700622104 · Cited by 918

A large language model for electronic health records

Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847

Conjunctive representations in learning and memory: Principles of cortical and hippocampal function.

O'Reilly, R. C., & Rudy, J. W. (2001) · Psychological Review · doi:10.1037//0033-295x.108.2.311 · Cited by 865

EEG frequency bands in psychiatric disorders: a review of resting state studies

Newson, J. J., & Thiagarajan, T. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00521 · Cited by 863

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016) · Scientific Reports · doi:10.1038/srep27755 · Cited by 716

Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells

Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.2.1017 · Cited by 741

Identifying facial phenotypes of genetic disorders using deep learning

Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739

Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

Owens, A., & Efros, A. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_39 · Cited by 704

Detecting and classifying lesions in mammograms with Deep Learning

Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-22437-z · Cited by 696

Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models

Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661

Neural Network Methods for Natural Language Processing

Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00479-8 · Cited by 664

scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data

Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617

Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model

Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A., Jahangir, E., et al. (2023) · doi:10.21203/rs.3.rs-2566942/v1 · Cited by 598

Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579

BEHRT: Transformer for Electronic Health Records

Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595

Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

Bruzzone, L., & Marconcini, M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.57 · Cited by 591

The Cost of Accumulating Evidence in Perceptual Decision Making

Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4010-11.2012 · Cited by 577

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Yang, T.-J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_18 · Cited by 572

Wishbone identifies bifurcating developmental trajectories from single-cell data

Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., & Covert, M. W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005177 · Cited by 557

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570

The rise of intelligent matter

Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021) · Nature · doi:10.1038/s41586-021-03453-y · Cited by 526

Deep learning interpretation of echocardiograms

Ghorbani, A., Ouyang, D., Abid, A., He, B., Chen, J. H., Harrington, R. A., Liang, D. H., Ashley, E. A., & Zou, J. Y. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0216-8 · Cited by 518

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646

End-to-end privacy preserving deep learning on multi-institutional medical imaging

Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00337-8 · Cited by 513

Leveraging uncertainty information from deep neural networks for disease detection

Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510

Learning and selective attention

Dayan, P., Kakade, S., & Montague, P. R. (2000) · Nature Neuroscience · doi:10.1038/81504 · Cited by 495

Screening cell–cell communication in spatial transcriptomics via collective optimal transport

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Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis

Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-3-12 · Cited by 480

Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity

Henegar, C., Tordjman, J., Achard, V., Lacasa, D., Cremer, I., Guerre-Millo, M., Poitou, C., Basdevant, A., Stich, V., Viguerie, N., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-1-r14 · Cited by 471

Regulating ChatGPT and other Large Generative AI Models

Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466

BioWordVec, improving biomedical word embeddings with subword information and MeSH

Zhang, Y., Chen, Q., Yang, Z., Lin, H., & Lu, Z. (2019) · Scientific Data · doi:10.1038/s41597-019-0055-0 · Cited by 455

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-42294-8 · Cited by 455

Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space

Nicolaou, M. A., Gunes, H., & Pantic, M. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.9 · Cited by 450

GPT-4 passes the bar exam

Katz, D. M., Bommarito, M. J., Gao, S., & Arredondo, P. (2024) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2023.0254 · Cited by 420

AI in Health: State of the Art, Challenges, and Future Directions

Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398

Edgenext: Efficiently amalgamated CNN-transformer architecture for mobile vision applications

Maaz, M., Shaker, A., Cholakkal, H., Khan, S., Zamir, S. W., Anwer, R. M., & Shahbaz Khan, F. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25082-8_1 · Cited by 407

Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390

In vitro neurons learn and exhibit sentience when embodied in a simulated game-world

Kagan, B. J., Kitchen, A. C., Tran, N. T., Habibollahi, F., Khajehnejad, M., Parker, B. J., Bhat, A., Rollo, B., Razi, A., & Friston, K. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.001 · Cited by 400

Stochastic dynamic programming with factored representations

Boutilier, C., Dearden, R., & Goldszmidt, M. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(00)00033-3 · Cited by 361

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363

Rethinking extinction

Dunsmoor, J. E., Niv, Y., Daw, N., & Phelps, E. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.028 · Cited by 329

Data Augmentation techniques in time series domain: a survey and taxonomy

Iglesias, G., Talavera, E., González-Prieto, Á., Mozo, A., & Gómez-Canaval, S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08459-3 · Cited by 347

Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype

Couture, H. D., Williams, L. A., Geradts, J., Nyante, S. J., Butler, E. N., Marron, J. S., Perou, C. M., Troester, M. A., & Niethammer, M. (2018) · npj Breast Cancer · doi:10.1038/s41523-018-0079-1 · Cited by 327

Could a neuroscientist understand a microprocessor?

Jonas, E., & Paul Kording, K. (2016) · doi:10.1101/055624 · Cited by 329

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Lenselink, E. B., ten Dijke, N., Bongers, B., Papadatos, G., van Vlijmen, H. W. T., Kowalczyk, W., IJzerman, A. P., & van Westen, G. J. P. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0232-0 · Cited by 334

A survey on data‐efficient algorithms in big data era

Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331

Brains and algorithms partially converge in natural language processing

Caucheteux, C., & King, J.-R. (2022) · Communications Biology · doi:10.1038/s42003-022-03036-1 · Cited by 318

BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Nessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003037 · Cited by 281

Habits, action sequences and reinforcement learning

Dezfouli, A., & Balleine, B. W. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08050.x · Cited by 297

Hierarchical motor control in mammals and machines

Merel, J., Botvinick, M., & Wayne, G. (2019) · Nature Communications · doi:10.1038/s41467-019-13239-6 · Cited by 274

Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics

Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006387 · Cited by 276

Bird species recognition using support vector machines

Fagerlund, S. (2007) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2007/38637 · Cited by 276

Hotspot identifies informative gene modules across modalities of single-cell genomics

DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267

Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study

Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2210461 · Cited by 262

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

Cang, Z., Mu, L., & Wei, G.-W. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005929 · Cited by 257

Weakly-supervised learning for lung carcinoma classification using deep learning

Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O., & Tsuneki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-66333-x · Cited by 250