Advances in neural information processing systems 7

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Ensemble methods in machine learning

Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408

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

Dynamic Graph CNN for Learning on Point Clouds

Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199

Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Sherstinsky, A. (2020) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2019.132306 · Cited by 5,680

Machine Learning in Medicine

Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579

Microstructure of a spatial map in the entorhinal cortex

Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562

Hierarchical models of object recognition in cortex

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

Incremental Learning for Robust Visual Tracking

Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061

Reservoir computing approaches to recurrent neural network training

Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956

Popular Ensemble Methods: An Empirical Study

Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953

Methods for interpreting and understanding deep neural networks

Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111

Neuronal Synchrony: A Versatile Code for the Definition of Relations?

Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167

Stanley: The robot that won the DARPA Grand Challenge

Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20147 · Cited by 2,237

Using mutual information for selecting features in supervised neural net learning

Battiti, R. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.298224 · Cited by 2,148

Model compression

Buciluǎ, C., Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150464 · Cited by 2,068

Path integration and the neural basis of the 'cognitive map'

McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I., & Moser, M.-B. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1932 · Cited by 2,024

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Delorme, A., Sejnowski, T., & Makeig, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.004 · Cited by 1,759

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704

Early Stopping - But When?

Prechelt, L. (1998) · Lecture Notes in Computer Science · doi:10.1007/3-540-49430-8_3 · Cited by 1,649

Reconciling modern machine-learning practice and the classical bias–variance trade-off

Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903070116 · Cited by 1,595

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

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys · doi:10.1145/3605943 · Cited by 1,387

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al. (2023) · ACM Computing Surveys · doi:10.1145/3561048 · Cited by 1,386

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

Deep Learning on Image Denoising: An overview

Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091

ProteinBERT: a universal deep-learning model of protein sequence and function

Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac020 · Cited by 1,009

Efficient transformers: A survey

Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023) · ACM Computing Surveys · doi:10.1145/3530811 · Cited by 1,267

Ensemble learning for data stream analysis: A survey

Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050

Object Recognition with Gradient-Based Learning

LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-46805-6_19 · Cited by 947

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

Feature visualization

Olah, C., Mordvintsev, A., & Schubert, L. (2017) · Distill · doi:10.23915/distill.00007 · Cited by 880

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834

Prognostics methods for battery health monitoring using a bayesian framework

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831

Neuromodulation: acetylcholine and memory consolidation

Hasselmo, M. E. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01365-0 · Cited by 809

Formal theory of creativity, fun, and intrinsic motivation (1990–2010)

Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747

A Survey on Federated Learning for Resource-Constrained IoT Devices

Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774

Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells

Silberberg, G., & Markram, H. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.012 · Cited by 768

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749

Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763

Path integration in mammals

Etienne, A. S., & Jeffery, K. J. (2004) · Hippocampus · doi:10.1002/hipo.10173 · Cited by 726

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707

A semantic matching energy function for learning with multi-relational data: Application to word-sense disambiguation

Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2014) · Machine Learning · doi:10.1007/s10994-013-5363-6 · Cited by 675

Deep learning for molecular design—a review of the state of the art

Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683

Loss odyssey in medical image segmentation

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662

Large language models for software engineering: survey and open problems

Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606

Synchrony Unbound

Shadlen, M. N., & Movshon, J. A. (1999) · Neuron · doi:10.1016/s0896-6273(00)80822-3 · Cited by 606

Twenty Years of Mixture of Experts

Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606

Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories

Barnes, T. D., Kubota, Y., Hu, D., Jin, D. Z., & Graybiel, A. M. (2005) · Nature · doi:10.1038/nature04053 · Cited by 595

The Building Blocks of Interpretability

Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018) · Distill · doi:10.23915/distill.00010 · Cited by 591

Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., Chartier, J., Dougherty, M. E., Liu, P. M., Abrams, G. M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2027540 · Cited by 582

Understanding adversarial attacks on deep learning based medical image analysis systems

Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., & Lu, F. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107332 · Cited by 553

Machine learning in medical applications: a review of state-of-the-art methods

Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542

Diffusion policy: Visuomotor policy learning via action diffusion

Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., & Song, S. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649241273668 · Cited by 613

The Temporal Correlation Hypothesis of Visual Feature Integration

Gray, C. M. (1999) · Neuron · doi:10.1016/s0896-6273(00)80820-x · Cited by 541

Generalized Out-of-Distribution Detection: A Survey

Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884

Large Language Models for Software Engineering: Survey and Open Problems

Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · arXiv · doi:10.48550/arxiv.2310.03533 · Cited by 523

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508

ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505

The What and Why of Binding

von der Malsburg, C. (1999) · Neuron · doi:10.1016/s0896-6273(00)80825-9 · Cited by 501

Untitled work

peng (2017) · doi:10.1145/3072959.3073602 · Cited by 502

Six Human-Centered Artificial Intelligence Grand Challenges

Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S. M., Garibay, I., Grieman, K., et al. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2153320 · Cited by 501

Explaining anomalies detected by autoencoders using Shapley Additive Explanations

Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115736 · Cited by 496

Overfitting and neural networks: conjugate gradient and backpropagation

Lawrence, S., & Giles, C. L. (2000) · Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium · doi:10.1109/ijcnn.2000.857823 · Cited by 491

Learning the protein language: Evolution, structure, and function

Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480

Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding

Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467075 · Cited by 464

CADD v1.7: Using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

Schubach, M., Maass, T., Nazaretyan, L., Röner, S., & Kircher, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad989 · Cited by 455

Reward is enough

Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103535 · Cited by 445

Weak adversarial networks for high-dimensional partial differential equations

Zang, Y., Bao, G., Ye, X., & Zhou, H. (2020) · Journal of Computational Physics · doi:10.1016/j.jcp.2020.109409 · Cited by 444

Causal Intervention for Leveraging Popularity Bias in Recommendation

Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462875 · Cited by 443

Tweet sentiment analysis with classifier ensembles

da Silva, N. F. F., Hruschka, E. R., & Hruschka, E. R. (2014) · Decision Support Systems · doi:10.1016/j.dss.2014.07.003 · Cited by 431

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht, S. V., & Stone, P. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.01.002 · Cited by 417

In-Context Retrieval-Augmented Language Models

Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00605 · Cited by 449

Prevalence of neural collapse during the terminal phase of deep learning training

Papyan, V., Han, X. Y., & Donoho, D. L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015509117 · Cited by 403

Bayesian approach for neural networks—review and case studies

Lampinen, J., & Vehtari, A. (2001) · Neural Networks · doi:10.1016/s0893-6080(00)00098-8 · Cited by 392

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599295 · Cited by 387

Privacy preservation in federated learning: An insightful survey from the GDPR perspective

Truong, N., Sun, K., Wang, S., Guitton, F., & Guo, Y. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102402 · Cited by 399

Head direction, place, and movement correlates for cells in the rat retrosplenial cortex.

Cho, J., & Sharp, P. E. (2001) · Behavioral Neuroscience · doi:10.1037/0735-7044.115.1.3 · Cited by 385

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387

Generative Adversarial Networks: A Survey Toward Private and Secure Applications

Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., & Pan, Y. (2022) · ACM Computing Surveys · doi:10.1145/3459992 · Cited by 376

The emergence of the social brain network: evidence from typical and atypical development

JOHNSON, M. H., GRIFFIN, R., CSIBRA, G., HALIT, H., FARRONI, T., DE HAAN, M., TUCKER, L. A., BARON–COHEN, S., & RICHARDS, J. (2005) · Development and Psychopathology · doi:10.1017/s0954579405050297 · Cited by 369

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

Towards multi-modal causability with graph neural networks enabling information fusion for explainable ai

Holzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.008 · Cited by 352

Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad493 · Cited by 359

A computational theory of hippocampal function, and tests of the theory: New developments

Kesner, R. P., & Rolls, E. T. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.11.009 · Cited by 347

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345

A coupled attractor model of the rodent head direction system

Redish, A. D., Elga, A. N., & Touretzky, D. S. (1996) · Network: Computation in Neural Systems · doi:10.1088/0954-898x_7_4_004 · Cited by 344

Dissociating language and thought in large language models

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345

Generative Adversarial Networks in Time Series: A Systematic Literature Review

Brophy, E., Wang, Z., She, Q., & Ward, T. (2023) · ACM Computing Surveys · doi:10.1145/3559540 · Cited by 337

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

Tran, R., Lan, J., Shuaibi, M., Wood, B. M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., et al. (2023) · ACS Catalysis · doi:10.1021/acscatal.2c05426 · Cited by 329