Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference

Gödel Machines: Fully Self-referential Optimal Universal Self-improvers

Schmidhuber, J. (2007) · Cognitive Technologies · doi:10.1007/978-3-540-68677-4_7 · Cited by 110

Masked inverse folding with sequence transfer for protein representation learning

(2022) · doi:10.1093/protein/gzad015 · Cited by 110

Efficient duration and hierarchical modeling for human activity recognition

Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 115

Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

Wang, T., Liao, X., Chow, K. P., Lin, X., & Wang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3699710 · Cited by 107

Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces

Dangi, S., Orsborn, A. L., Moorman, H. G., & Carmena, J. M. (2013) · Neural Computation · doi:10.1162/neco_a_00460 · Cited by 103

Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

Ma, T., & Zhang, A. (2019) · BMC Genomics · doi:10.1186/s12864-019-6285-x · Cited by 105

MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology

Yang, S., Wang, Y., & Chen, H. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72083-3_28 · Cited by 114

Semantic anomaly detection with large language models

Elhafsi, A., Sinha, R., Agia, C., Schmerling, E., Nesnas, I. A. D., & Pavone, M. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10132-6 · Cited by 101

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Millidge, B., Tschantz, A., & Buckley, C. L. (2022) · Neural Computation · doi:10.1162/neco_a_01497 · Cited by 95

Active Machine Learning for Consideration Heuristics

Dzyabura, D., & Hauser, J. R. (2011) · Marketing Science · doi:10.1287/mksc.1110.0660 · Cited by 92

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

Ecker, A. S., Denfield, G. H., Bethge, M., & Tolias, A. S. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2044-15.2016 · Cited by 88

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance

Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102135 · Cited by 90

Child: A First Step Towards Continual Learning

Ring, M. B. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_11 · Cited by 84

Towards Scaling Fully Personalized PageRank

Fogaras, D., & Rácz, B. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30216-2_9 · Cited by 84

Untitled work

Liu, X.-Y., Yang, H., Gao, J., & Wang, C. D. (2021) · Proceedings of the Second ACM International Conference on AI in Finance · doi:10.1145/3490354.3494366 · Cited by 87

A novel database of children's spontaneous facial expressions (LIRIS-CSE)

Khan, R. A., Crenn, A., Meyer, A., & Bouakaz, S. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.02.004 · Cited by 82

Models and Algorithms for Tracking of Maneuvering Objects Using Variable Rate Particle Filters

Godsill, S. J., Vermaak, J., Ng, W., & Li, J. F. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.894708 · Cited by 81

Inferring input nonlinearities in neural encoding models

Ahrens, M. B., Paninski, L., & Sahani, M. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980701813936 · Cited by 72

Pre-trained models for detection and severity level classification of dysarthria from speech

Javanmardi, F., Kadiri, S. R., & Alku, P. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103047 · Cited by 74

Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap

Spiwok, V., & Králová, B. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3660208 · Cited by 70

Dependency parsing of learner English

Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A. (2018) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.16080.hua · Cited by 68

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C. A., Madadgar, S., Papalexiou, S. M., Davis, S. J., et al. (2022) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2021.0288 · Cited by 67

A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation

Mohamed, E., Sirlantzis, K., & Howells, G. (2022) · Displays · doi:10.1016/j.displa.2022.102239 · Cited by 67

Machine learning action parameters in lattice quantum chromodynamics

Shanahan, P. E., Trewartha, D., & Detmold, W. (2018) · Physical Review D · doi:10.1103/physrevd.97.094506 · Cited by 65

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00032 · Cited by 62

Deep learning for low-data drug discovery: Hurdles and opportunities

van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102818 · Cited by 61

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

Lan, A. S., Vats, D., Waters, A. E., & Baraniuk, R. G. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724664 · Cited by 60

I/O-efficient techniques for computing pagerank

Chen, Y.-Y., Gan, Q., & Suel, T. (2002) · Proceedings of the eleventh international conference on Information and knowledge management · doi:10.1145/584792.584882 · Cited by 59

Explainable AI and Causal Understanding: Counterfactual Approaches Considered

Baron, S. (2023) · Minds and Machines · doi:10.1007/s11023-023-09637-x · Cited by 58

Measuring the stability of results from supervised statistical learning

Philipp, M., Rusch, T., Hornik, K., & Strobl, C. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2018.1473779 · Cited by 56

Full Gibbs Sampling procedure for Bayesian system identification incorporating sparse Bayesian learning with Automatic Relevance Determination

Huang, Y., & Beck, J. L. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12358 · Cited by 53

FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation

Hofstätter, S., Chen, J., Raman, K., & Zamani, H. (2022) · arXiv · doi:10.48550/arxiv.2209.14290 · Cited by 51

FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation

Hofstätter, S., Chen, J., Raman, K., & Zamani, H. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591687 · Cited by 51

Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems

Xu, Q., Ali, S., & Yue, T. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3582571 · Cited by 49

Fine-tuning GPT-3 for legal rule classification

Liga, D., & Robaldo, L. (2023) · Computer Law & Security Review · doi:10.1016/j.clsr.2023.105864 · Cited by 48

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Smith, Z., Strobel, M., Vani, B. P., & Tiwary, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01698 · Cited by 47

Language models: past, present, and future

Li, H. (2022) · Communications of the ACM · doi:10.1145/3490443 · Cited by 45

Estimating smile intensity: A better way

Girard, J. M., Cohn, J. F., & De la Torre, F. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.004 · Cited by 44

Dopamine, inference, and uncertainty

Gershman, S. J. (2017) · Neural Computation · doi:10.1162/neco_a_01023 · Cited by 40

Mining clinical phrases from nursing notes to discover risk factors of patient deterioration

Korach, Z. T., Yang, J., Rossetti, S. C., Cato, K. D., Kang, M.-J., Knaplund, C., Schnock, K. O., Garcia, J. P., Jia, H., Schwartz, J. M., et al. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104053 · Cited by 40

Fair navigation planning: A resource for characterizing and designing fairness in mobile robots

Brandão, M., Jirotka, M., Webb, H., & Luff, P. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103259 · Cited by 35

Multi-level transfer learning for improving the performance of deep neural networks: Theory and practice from the tasks of facial emotion recognition and named entity recognition

Hung, J. C., & Chang, J.-W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107491 · Cited by 35

Micro-analysis of infant looking in a naturalistic social setting: insights from biologically based models of attention

de Barbaro, K., Chiba, A., & Deák, G. O. (2011) · Developmental Science · doi:10.1111/j.1467-7687.2011.01066.x · Cited by 34

Causal Structure Learning: A Combinatorial Perspective

Squires, C., & Uhler, C. (2023) · Foundations of Computational Mathematics · doi:10.1007/s10208-022-09581-9 · Cited by 31

Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets

Ma, S., Song, K., Niu, M., Tian, H., & Yan, Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02051-7 · Cited by 31

Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity

Pedroni, B. U., Joshi, S., Deiss, S. R., Sheik, S., Detorakis, G., Paul, S., Augustine, C., Neftci, E. O., & Cauwenberghs, G. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00357 · Cited by 29

Evaluation of Methods to Detect Shifts in Directional Selection at the Genome Scale

(2022) · doi:10.1093/molbev/msac247 · Cited by 30

Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Nakhle, F., & Harfouche, A. L. (2021) · Patterns · doi:10.1016/j.patter.2021.100323 · Cited by 28

Tensor networks contraction and the belief propagation algorithm

Alkabetz, R., & Arad, I. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.023073 · Cited by 27

A review of computational intelligence techniques in coral reef-related applications

Salcedo-Sanz, S., Cuadra, L., & Vermeij, M. J. A. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.008 · Cited by 27

Deep learning methods for 3D structural proteome and interactome modeling

Lee, D., Xiong, D., Wierbowski, S., Li, L., Liang, S., & Yu, H. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102329 · Cited by 26

Bayesian optimization for ternary complex prediction (BOTCP)

Rao, A., Tunjic, T. M., Brunsteiner, M., Müller, M., Fooladi, H., Gasbarri, C., & Weber, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100072 · Cited by 25

Generating Executable Action Plans with Environmentally-Aware Language Models

Gramopadhye, M., & Szafir, D. (2023) · 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341989 · Cited by 26

Robust 3D face modeling and reconstruction from frontal and side images

Jin, H., Wang, X., Zhong, Z., & Hua, J. (2017) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2016.11.001 · Cited by 24

Relational memory-augmented language models

Liu, Q., Yogatama, D., & Blunsom, P. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00476 · Cited by 23

On the Role of Negative Precedent in Legal Outcome Prediction

Valvoda, J., Cotterell, R., & Teufel, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00532 · Cited by 19

Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection

Mera, C., Orozco-Alzate, M., & Branch, J. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.04.006 · Cited by 18

Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657749 · Cited by 17

Quantifying uncertainty in deep learning approaches to radio galaxy classification

Mohan, D., Scaife, A. M. M., Porter, F., Walmsley, M., & Bowles, M. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac223 · Cited by 16

Predictive Representations: Building Blocks of Intelligence

Carvalho, W., Tomov, M. S., de Cothi, W., Barry, C., & Gershman, S. J. (2024) · Neural Computation · doi:10.1162/neco_a_01705 · Cited by 15

Towards Robust QA Evaluation via Open LLMs

Kamalloo, E., Upadhyay, S., & Lin, J. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657675 · Cited by 15

How could we make a social robot? A virtual bargaining approach

Chater, N. (2023) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2022.0040 · Cited by 14

Cyberbully Detection Using BERT with Augmented Texts

Guo, X., Anjum, U., & Zhan, J. (2022) · 2022 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata55660.2022.10020581 · Cited by 12

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies.

Al-Hussaini, I., Nakajima An, D., Lee, A. J., Bi, S., & Mitchell, C. S. (2022) · 2022 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata55660.2022.10020807 · Cited by 10

An associative memory integrated system for character recognition

Pouliquen, P. O., Andreou, A. G., Strohbehn, K., & Jenkins, R. E. (2002) · Proceedings of 36th Midwest Symposium on Circuits and Systems · doi:10.1109/mwscas.1993.342935 · Cited by 10

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00549 · Cited by 9

Improved contourlet transform construction and its application to surface defect recognition of metals

Liu, X., Xu, K., Zhou, D., & Zhou, P. (2020) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-020-00720-5 · Cited by 9

Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

Fishchuk, V., & Braun, D. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10144-2 · Cited by 7

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning

Haupt, A., Christoffersen, P., Damani, M., & Hadfield-Menell, D. (2024) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-024-09682-5 · Cited by 5