Advances in neural information processing systems 7

Automatic facial expression recognition on a single 3D face by exploring shape deformation

Gong, B., Wang, Y., Liu, J., & Tang, X. (2009) · Proceedings of the 17th ACM international conference on Multimedia · doi:10.1145/1631272.1631358 · Cited by 116

A review of deep learning‐based approaches for deepfake content detection

(2024) · doi:10.1111/exsy.13570 · Cited by 116

Analyzing the quality and challenges of uncertainty estimations for brain tumor segmentation

Jungo, A., Balsiger, F., & Reyes, M. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00282 · Cited by 115

Utilizing Electronic Health Records to Predict Acute Kidney Injury Risk and Outcomes: Workgroup Statements from the 15 th ADQI Consensus Conference

Sutherland, S. M., Chawla, L. S., Kane-Gill, S. L., Hsu, R. K., Kramer, A. A., Goldstein, S. L., Kellum, J. A., Ronco, C., Bagshaw, S. M., & on behalf of the 15 ADQI Consensus Group (2016) · Canadian Journal of Kidney Health and Disease · doi:10.1186/s40697-016-0099-4 · Cited by 115

Memory-aware curriculum federated learning for breast cancer classification

Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107318 · Cited by 114

Multiple sequence alignment-based RNA language model and its application to structural inference

Zhang, Y., Lang, M., Jiang, J., Gao, Z., Xu, F., Litfin, T., Chen, K., Singh, J., Huang, X., Song, G., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1031 · Cited by 112

Network planning with deep reinforcement learning

Zhu, H., Gupta, V., Ahuja, S. S., Tian, Y., Zhang, Y., & Jin, X. (2021) · Proceedings of the 2021 ACM SIGCOMM 2021 Conference · doi:10.1145/3452296.3472902 · Cited by 112

Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms

Koshiyama, A., Kazim, E., Treleaven, P., Rai, P., Szpruch, L., Pavey, G., Ahamat, G., Leutner, F., Goebel, R., Knight, A., et al. (2024) · Royal Society Open Science · doi:10.1098/rsos.230859 · Cited by 110

A survey of multimodal deep generative models

Suzuki, M., & Matsuo, Y. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2035253 · Cited by 109

Beyond generalization: a theory of robustness in machine learning

Freiesleben, T., & Grote, T. (2023) · Synthese · doi:10.1007/s11229-023-04334-9 · Cited by 106

Language Model Behavior: A Comprehensive Survey

Chang, T. A., & Bergen, B. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00492 · Cited by 105

Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach

Kocoń, J., Figas, A., Gruza, M., Puchalska, D., Kajdanowicz, T., & Kazienko, P. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102643 · Cited by 103

A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

Michaels, J. A., Schaffelhofer, S., Agudelo-Toro, A., & Scherberger, H. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005087117 · Cited by 103

Quantum optimization with a novel Gibbs objective function and ansatz architecture search

Li, L., Fan, M., Coram, M., Riley, P., & Leichenauer, S. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023074 · Cited by 98

Attention fusion network for multi-spectral semantic segmentation

Xu, J., Lu, K., & Wang, H. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.015 · Cited by 99

AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories

Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214460 · Cited by 97

Shortcut Learning of Large Language Models in Natural Language Understanding

Du, M., He, F., Zou, N., Tao, D., & Hu, X. (2024) · Communications of the ACM · doi:10.1145/3596490 · Cited by 94

Privacy-preserving and communication-efficient federated learning in Internet of Things

Fang, C., Guo, Y., Hu, Y., Ma, B., Feng, L., & Yin, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102199 · Cited by 93

Learning and inference on generative adversarial quantum circuits

Zeng, J., Wu, Y., Liu, J.-G., Wang, L., & Hu, J. (2019) · Physical Review A · doi:10.1103/physreva.99.052306 · Cited by 94

Multimodal contrastive learning for spatial gene expression prediction using histology images

Min, W., Shi, Z., Zhang, J., Wan, J., & Wang, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae551 · Cited by 90

Ring Attractor Dynamics Emerge from a Spiking Model of the Entire Protocerebral Bridge

Kakaria, K. S., & de Bivort, B. L. (2017) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2017.00008 · Cited by 89

Satellite Pose Estimation Competition 2021: Results and Analyses

Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin, T.-J., Izzo, D., & D’Amico, S. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.01.002 · Cited by 91

Detecting fake images by identifying potential texture difference

Yang, J., Xiao, S., Li, A., Lan, G., & Wang, H. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.043 · Cited by 86

Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities

Zeng, J., Liu, T., & Zhou, J. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3532064 · Cited by 94

Security in smart cities: A brief review of digital forensic schemes for biometric data

Ross, A., Banerjee, S., & Chowdhury, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.07.009 · Cited by 85

Improving Human-AI Collaboration With Descriptions of AI Behavior

Cabrera, Á. A., Perer, A., & Hong, J. I. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579612 · Cited by 87

Predicting inter-hemispheric transfer time from the diffusion properties of the corpus callosum in healthy individuals and schizophrenia patients: A combined ERP and DTI study

Whitford, T. J., Kubicki, M., Ghorashi, S., Schneiderman, J. S., Hawley, K. J., McCarley, R. W., Shenton, M. E., & Spencer, K. M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.048 · Cited by 83

Deep balanced discrete hashing for image retrieval

Zheng, X., Zhang, Y., & Lu, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.037 · Cited by 83

When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs

Kamoi, R., Zhang, Y., Zhang, N., Han, J., & Zhang, R. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00713 · Cited by 200

Generating meaning: active inference and the scope and limits of passive AI

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.10.002 · Cited by 80

Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning

Alizadeh, M., Kubli, M., Samei, Z., Dehghani, S., Zahedivafa, M., Bermeo, J. D., Korobeynikova, M., & Gilardi, F. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-024-00345-9 · Cited by 80

The Future of Research in an Artificial Intelligence-Driven World

Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., et al. (2024) · Journal of Management Inquiry · doi:10.1177/10564926231219622 · Cited by 80

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

Wang, D., Liang, J., Ye, J., Li, J., Li, J., Zhang, Q., Hu, Q., Pan, C., Wang, D., Liu, Z., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/58041 · Cited by 76

Longitudinal self-supervised learning

Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102051 · Cited by 71

Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Adlakha, V., BehnamGhader, P., Lu, X. H., Meade, N., & Reddy, S. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00667 · Cited by 74

Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection

Hsu, C.-Y., & Li, W. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2023.2191256 · Cited by 75

Information generation as a functional basis of consciousness

Kanai, R., Chang, A., Yu, Y., Magrans de Abril, I., Biehl, M., & Guttenberg, N. (2019) · Neuroscience of Consciousness · doi:10.1093/nc/niz016 · Cited by 72

Large language models for life cycle assessments: Opportunities, challenges, and risks

Preuss, N., Alshehri, A. S., & You, F. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142824 · Cited by 71

Effects of theta transcranial alternating current stimulation over the frontal cortex on reversal learning

Wischnewski, M., Zerr, P., & Schutter, D. J. L. G. (2016) · Brain Stimulation · doi:10.1016/j.brs.2016.04.011 · Cited by 62

Embedding Regression: Models for Context-Specific Description and Inference

RODRIGUEZ, P. L., SPIRLING, A., & STEWART, B. M. (2023) · American Political Science Review · doi:10.1017/s0003055422001228 · Cited by 70

Optimization-based Prompt Injection Attack to LLM-as-a-Judge

Shi, J., Yuan, Z., Liu, Y., Huang, Y., Zhou, P., Sun, L., & Gong, N. Z. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3658644.3690291 · Cited by 70

Survey on AI-generated plagiarism detection: The impact of large language models on academic integrity

Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Llorens Salvador, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09576-x · Cited by 70

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems

FitzGerald, J., Ananthakrishnan, S., Arkoudas, K., Bernardi, D., Bhagia, A., Delli Bovi, C., Cao, J., Chada, R., Chauhan, A., Chen, L., et al. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539173 · Cited by 68

Harnessing large language models to develop research-based learning assistants for formative feedback

Steinert, S., Avila, K. E., Ruzika, S., Kuhn, J., & Küchemann, S. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00354-1 · Cited by 73

ChatGPT in finance: Applications, challenges, and solutions

Khan, M. S., & Umer, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24890 · Cited by 67

Using AI-based detectors to control AI-assisted plagiarism in ESL writing: “The Terminator Versus the Machines”

Ibrahim, K. (2023) · Language Testing in Asia · doi:10.1186/s40468-023-00260-2 · Cited by 66

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

Dynamic-based damage identification using neural network ensembles and damage index method

Dackermann, U., Li, J., & Samali, B. (2010) · Advances in Structural Engineering · doi:10.1260/1369-4332.13.6.1001 · Cited by 66

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

Electrophysiological assessment of auditory stimulus-specific plasticity in schizophrenia

Mears, R. P., & Spencer, K. M. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.12.016 · Cited by 64

A survey of model compression strategies for object detection

Lyu, Z., Yu, T., Pan, F., Zhang, Y., Luo, J., Zhang, D., Chen, Y., Zhang, B., & Li, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17192-x · Cited by 65

Scaling Data Generation in Vision-and-Language Navigation

Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · arXiv · doi:10.48550/arxiv.2307.15644 · Cited by 64

Information Closure Theory of Consciousness

Chang, A. Y. C., Biehl, M., Yu, Y., & Kanai, R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01504 · Cited by 64

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

Rafferty, C., Johnson, K., O'Mahony, J., Burgoyne, B., Rea, R., & Balss, K. M. (2020) · Biotechnology Progress · doi:10.1002/btpr.2977 · Cited by 63

Measuring Attribution in Natural Language Generation Models

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023) · Computational Linguistics · doi:10.1162/coli_a_00486 · Cited by 71

VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering

Wang, Y., Yasunaga, M., Ren, H., Wada, S., & Leskovec, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01973 · Cited by 68

Maintenance of Rat Head Direction Cell Firing During Locomotion in the Vertical Plane

Stackman, R. W., Tullman, M. L., & Taube, J. S. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.83.1.393 · Cited by 61

Human-in-the-Loop Machine Learning to Increase Video Accessibility for Visually Impaired and Blind Users

Yuksel, B. F., Fazli, P., Mathur, U., Bisht, V., Kim, S. J., Lee, J. J., Jin, S. J., Siu, Y.-T., Miele, J. A., & Yoon, I. (2020) · Proceedings of the 2020 ACM Designing Interactive Systems Conference · doi:10.1145/3357236.3395433 · Cited by 60

Interpretable generalized additive neural networks

Kraus, M., Tschernutter, D., Weinzierl, S., & Zschech, P. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.032 · Cited by 59

Scaling Data Generation in Vision-and-Language Navigation

Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01103 · Cited by 64

SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice

Singhal, M., Ling, C., Paudel, P., Thota, P., Kumarswamy, N., Stringhini, G., & Nilizadeh, S. (2023) · 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp57164.2023.00056 · Cited by 57

Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

Li, P., Yu, Y., Huang, D., Wang, Z., & Sharma, A. (2023) · Geophysical Research Letters · doi:10.1029/2023gl103405 · Cited by 57

Generative model benchmarks for superconducting qubits

Hamilton, K. E., Dumitrescu, E. F., & Pooser, R. C. (2019) · Physical Review A · doi:10.1103/physreva.99.062323 · Cited by 57

3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network

Hermoza, R., & Sipiran, I. (2018) · Proceedings of Computer Graphics International 2018 · doi:10.1145/3208159.3208173 · Cited by 56

Application of support vector regression for modeling low flow time series

Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56

Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks

Dackermann, U., Li, J., & Samali, B. (2013) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2013.02.018 · Cited by 55

End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Yuan, W., Hang, K., Kragic, D., Wang, M. Y., & Stork, J. A. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.06.007 · Cited by 54

Improving clinical outcome predictions using convolution over medical entities with multimodal learning

Bardak, B., & Tan, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102112 · Cited by 48

Target-Driven Structured Transformer Planner for Vision-Language Navigation

Zhao, Y., Chen, J., Gao, C., Wang, W., Yang, L., Ren, H., Xia, H., & Liu, S. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3548281 · Cited by 54

Privacy, accuracy, and model fairness trade-offs in federated learning

Gu, X., Tianqing, Z., Li, J., Zhang, T., Ren, W., & Choo, K.-K. R. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102907 · Cited by 53

Taxonomy of Machine Learning Safety: A Survey and Primer

Mohseni, S., Wang, H., Xiao, C., Yu, Z., Wang, Z., & Yadawa, J. (2023) · ACM Computing Surveys · doi:10.1145/3551385 · Cited by 53

AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset

Cai, Z., Ghosh, S., Adatia, A. P., Hayat, M., Dhall, A., Gedeon, T., & Stefanov, K. (2024) · Proceedings of the 32nd ACM International Conference on Multimedia · doi:10.1145/3664647.3680795 · Cited by 59

Agent design pattern catalogue: A collection of architectural patterns for foundation model based agents

Liu, Y., Lo, S. K., Lu, Q., Zhu, L., Zhao, D., Xu, X., Harrer, S., & Whittle, J. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112278 · Cited by 51

ProoFVer: Natural Logic Theorem Proving for Fact Verification

Krishna, A., Riedel, S., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00503 · Cited by 51

Apical amplification—a cellular mechanism of conscious perception?

Marvan, T., Polák, M., Bachmann, T., & Phillips, W. A. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab036 · Cited by 45

On Decoding Strategies for Neural Text Generators

Wiher, G., Meister, C., & Cotterell, R. (2022) · arXiv · doi:10.48550/arxiv.2203.15721 · Cited by 48

CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging

Hamamci, I. E., Er, S., & Menze, B. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72390-2_45 · Cited by 47

Support vector machine analysis of functional magnetic resonance imaging of interoception does not reliably predict individual outcomes of cognitive behavioral therapy in panic disorder with agoraphobia

Sundermann, B., Bode, J., Lueken, U., Westphal, D., Gerlach, A. L., Straube, B., Wittchen, H.-U., Ströhle, A., Wittmann, A., Konrad, C., et al. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00099 · Cited by 46

Considerations for a more ethical approach to data in AI: On data representation and infrastructure

Baird, A., & Schuller, B. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00025 · Cited by 46

Structured like a language model: Analysing AI as an automated subject

Magee, L., Arora, V., & Munn, L. (2023) · Big Data & Society · doi:10.1177/20539517231210273 · Cited by 46

An analysis of protein language model embeddings for fold prediction

Villegas-Morcillo, A., Gomez, A. M., & Sanchez, V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac142 · Cited by 46

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Togninalli, M., Wang, X., Kucera, T., Shrestha, S., Juliana, P., Mondal, S., Pinto, F., Govindan, V., Crespo-Herrera, L., Huerta-Espino, J., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad336 · Cited by 49

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation

Zhou, Q., Liu, C., Duan, Y., Sun, K., Li, Y., Kan, H., Gu, Z., Shu, J., & Hu, J. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1392555 · Cited by 44

Predictive coding networks for temporal prediction

Millidge, B., Tang, M., Osanlouy, M., Harper, N. S., & Bogacz, R. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011183 · Cited by 44

Predictive coding feedback results in perceived illusory contours in a recurrent neural network

Pang, Z., O’May, C. B., Choksi, B., & VanRullen, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.08.024 · Cited by 44

Interaction between COMT Val158Met polymorphism and childhood adversity affects reward processing in adulthood

Boecker-Schlier, R., Holz, N. E., Buchmann, A. F., Blomeyer, D., Plichta, M. M., Jennen-Steinmetz, C., Wolf, I., Baumeister, S., Treutlein, J., Rietschel, M., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.006 · Cited by 42

Efficient long-text understanding with short-text models

Ivgi, M., Shaham, U., & Berant, J. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00547 · Cited by 42

Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis

Roggeveen, L., el Hassouni, A., Ahrendt, J., Guo, T., Fleuren, L., Thoral, P., Girbes, A. R., Hoogendoorn, M., & Elbers, P. W. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.102003 · Cited by 41

Exploration-exploitation in multi-agent learning: Catastrophe theory meets game theory

Leonardos, S., & Piliouras, G. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2021.103653 · Cited by 39

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Pati, P., Jaume, G., Ayadi, Z., Thandiackal, K., Bozorgtabar, B., Gabrani, M., & Goksel, O. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102915 · Cited by 37

Interpretable-through-prototypes deepfake detection for diffusion models

Aghasanli, A., Kangin, D., & Angelov, P. (2023) · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00053 · Cited by 37

Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning

Le, B. M., & Woo, S. S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02045 · Cited by 36

Explainable AI for time series via Virtual Inspection Layers

Vielhaben, J., Lapuschkin, S., Montavon, G., & Samek, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110309 · Cited by 38

An AI toolkit to support teacher reflection

Phillips, T. M., Saleh, A., & Ozogul, G. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00295-1 · Cited by 36

Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Maheshwari, H., Liu, Y.-C., & Kira, Z. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00106 · Cited by 37

Reactivation of hippocampal ensemble activity patterns in the aging rat.

Gerrard, J. L., Kudrimoti, H., McNaughton, B. L., & Barnes, C. A. (2001) · Behavioral Neuroscience · doi:10.1037/0735-7044.115.6.1180 · Cited by 35

Learning robots to grasp by demonstration

De Coninck, E., Verbelen, T., Van Molle, P., Simoens, P., & Dhoedt, B. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103474 · Cited by 34

Mind and body: psychophysiological profiles of instructional and motivational self-talk

Bellomo, E., Cooke, A., Gallicchio, G., Ring, C., & Hardy, J. (2020) · Psychophysiology · doi:10.1111/psyp.13586 · Cited by 33

Top-Down Synthesis for Library Learning

Bowers, M., Olausson, T. X., Wong, L., Grand, G., Tenenbaum, J. B., Ellis, K., & Solar-Lezama, A. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3571234 · Cited by 33