Advances in neural information processing systems 7

Data Poisoning Attacks on Federated Machine Learning

Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128646 · Cited by 310

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

Loo, S. K., & Makeig, S. (2012) · Neurotherapeutics · doi:10.1007/s13311-012-0131-z · Cited by 306

Empowering biomedical discovery with AI agents

Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315

A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023) · ACM Computing Surveys · doi:10.1145/3527848 · Cited by 301

DeepFake Detection for Human Face Images and Videos: A Survey

Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022) · IEEE Access · doi:10.1109/access.2022.3151186 · Cited by 297

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Böhle, M., Eitel, F., Weygandt, M., & Ritter, K. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00194 · Cited by 291

Global Canopy Height Regression and Uncertainty Estimation from GEDI LIDAR Waveforms with Deep Ensembles

Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112760 · Cited by 291

Zoom In: An Introduction to Circuits

Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020) · Distill · doi:10.23915/distill.00024.001 · Cited by 288

Recent advances in surface defect inspection of industrial products using deep learning techniques

Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287

Federated learning in a medical context: A systematic literature review

Pfitzner, B., Steckhan, N., & Arnrich, B. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3412357 · Cited by 288

A Survey of Privacy Attacks in Machine Learning

Rigaki, M., & Garcia, S. (2024) · ACM Computing Surveys · doi:10.1145/3624010 · Cited by 285

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279

A Survey on Model Compression for Large Language Models

Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273

ViperGPT: Visual Inference via Python Execution for Reasoning

Surís, D., Menon, S., & Vondrick, C. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01092 · Cited by 273

ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models

Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., & Raffel, C. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00461 · Cited by 330

Long-term cancer survival prediction using multimodal deep learning

Vale-Silva, L. A., & Rohr, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-92799-4 · Cited by 268

Dynamic movement primitives in robotics: A tutorial survey

Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · 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

Trustworthy ai: A computational perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261

Mice alternate between discrete strategies during perceptual decision-making

Ashwood, Z. C., Roy, N. A., Stone, I. R., Laboratory, T. I. B., Urai, A. E., Churchland, A. K., Pouget, A., & Pillow, J. W. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-01007-z · Cited by 229

Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

Rudolph, M., Wehrbein, T., Rosenhahn, B., & Wandt, B. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00262 · Cited by 280

A Survey on Deepfake Video Detection

Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics · doi:10.1049/bme2.12031 · Cited by 253

Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models

Bailly, A., Blanc, C., Francis, É., Guillotin, T., Jamal, F., Wakim, B., & Roy, P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106504 · Cited by 252

An effective approach for software project effort and duration estimation with machine learning algorithms

Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2017.11.066 · Cited by 247

A high-level overview of AI ethics

Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269

Causal interpretability for machine learning-problems, methods and evaluation

Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238

Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions

Ennab, M., & Mcheick, H. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1444763 · Cited by 238

The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity

Pehle, C., Billaudelle, S., Cramer, B., Kaiser, J., Schreiber, K., Stradmann, Y., Weis, J., Leibfried, A., Müller, E., & Schemmel, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.795876 · Cited by 233

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462571 · Cited by 300

Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis

Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230526 · Cited by 224

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C.-K., Li, X., & Guan, C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3308189 · Cited by 224

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives

Cascella, M., Semeraro, F., Montomoli, J., Bellini, V., Piazza, O., & Bignami, E. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02045-3 · Cited by 219

A survey on few-shot class-incremental learning

Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220

Three Challenges for AI-Assisted Decision-Making

Steyvers, M., & Kumar, A. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231181102 · Cited by 217

Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies

Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211

DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors

Gao, W., Zhang, L., Teng, Q., He, J., & Wu, H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107728 · Cited by 214

Classifier calibration: a survey on how to assess and improve predicted class probabilities

Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · Machine Learning · doi:10.1007/s10994-023-06336-7 · Cited by 228

Coregulation of Transcription Factor Binding and Nucleosome Occupancy through DNA Features of Mammalian Enhancers

Barozzi, I., Simonatto, M., Bonifacio, S., Yang, L., Rohs, R., Ghisletti, S., & Natoli, G. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.04.006 · Cited by 209

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206

Global optimization of a neural network-hidden Markov model hybrid

Bengio, Y., De Mori, R., Flammia, G., & Kompe, R. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.125866 · Cited by 203

IoU-aware single-stage object detector for accurate localization

Wu, S., Li, X., & Wang, X. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103911 · Cited by 202

Combating disinformation in a social media age

Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, T. H., Ding, K., Karami, M., & Liu, H. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1385 · Cited by 198

Explainable decision forest: Transforming a decision forest into an interpretable tree

Sagi, O., & Rokach, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.013 · Cited by 189

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

Ghasemi, A., Hashtarkhani, S., Schwartz, D. L., & Shaban‐Nejad, A. (2024) · Cancer Innovation · doi:10.1002/cai2.136 · Cited by 193

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196

InferFix: End-to-End Program Repair with LLMs

Jin, M., Shahriar, S., Tufano, M., Shi, X., Lu, S., Sundaresan, N., & Svyatkovskiy, A. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3613892 · Cited by 191

TweepFake: about detecting deepfake tweets

Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251415 · Cited by 185

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188

Learning structures: Predictive representations, replay, and generalization

Momennejad, I. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.02.017 · Cited by 159

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., et al. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00447 · Cited by 179

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.12028 · Cited by 179

Large Language Models and the Reverse Turing Test

Sejnowski, T. J. (2023) · Neural Computation · doi:10.1162/neco_a_01563 · Cited by 169

SinSR: Diffusion-Based Image Super-Resolution in a Single Step

Wang, Y., Yang, W., Chen, X., Wang, Y., Guo, L., Chau, L.-P., Liu, Z., Qiao, Y., Kot, A. C., & Wen, B. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02437 · Cited by 196

Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework

Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100702 · Cited by 170

Prediction of students’ early dropout based on their interaction logs in online learning environment

Mubarak, A. A., Cao, H., & Zhang, W. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1727529 · Cited by 170

Bayes in the Brain—On Bayesian Modelling in Neuroscience

Colombo, M., & Seriès, P. (2012) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axr043 · Cited by 164

Fake face detection via adaptive manipulation traces extraction network

Guo, Z., Yang, G., Chen, J., & Sun, X. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103170 · Cited by 165

Data extraction for evidence synthesis using a large language model: A proof-of-concept study

Gartlehner, G., Kahwati, L., Hilscher, R., Thomas, I., Kugley, S., Crotty, K., Viswanathan, M., Nussbaumer‐Streit, B., Booth, G., Erskine, N., et al. (2024) · Research Synthesis Methods · doi:10.1002/jrsm.1710 · Cited by 164

Spatial goal coding in the hippocampal formation

Nyberg, N., Duvelle, É., Barry, C., & Spiers, H. J. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.012 · Cited by 141

Evaluating saliency map explanations for convolutional neural networks: a user study

Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., & Berthouze, N. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377519 · Cited by 163

Artificial intelligence and machine learning for quantum technologies

Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162

Performance vs. competence in human–machine comparisons

Firestone, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905334117 · Cited by 150

Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Sui, Y., Zhou, M., Zhou, M., Han, S., & Zhang, D. (2024) · Proceedings of the 17th ACM International Conference on Web Search and Data Mining · doi:10.1145/3616855.3635752 · Cited by 155

"I wouldn't say offensive but...": Disability-Centered Perspectives on Large Language Models

Gadiraju, V., Kane, S., Dev, S., Taylor, A., Wang, D., Denton, R., & Brewer, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3593989 · Cited by 155

SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos

Liu, H., Teng, Y., Lu, T., Wang, H., & Wang, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01703 · Cited by 154

Weaponized AI for cyber attacks

Yamin, M. M., Ullah, M., Ullah, H., & Katt, B. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102722 · Cited by 154

Conditional out-of-distribution generation for unpaired data using transfer VAE

Lotfollahi, M., Naghipourfar, M., Theis, F. J., & Wolf, F. A. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa800 · Cited by 130

Theory Is All You Need: AI, Human Cognition, and Causal Reasoning

(2024) · doi:10.1287/stsc.2024.0189 · Cited by 151

Bidirectional synaptic plasticity rapidly modifies hippocampal representations

Milstein, A. D., Li, Y., Bittner, K. C., Grienberger, C., Soltesz, I., Magee, J. C., & Romani, S. (2021) · eLife · doi:10.7554/elife.73046 · Cited by 151

Variational autoencoders for cancer data integration: design principles and computational practice

Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & Liò, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01205 · Cited by 145

A Survey of Techniques for Optimizing Transformer Inference

Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154

Metaphoria: An Algorithmic Companion for Metaphor Creation

Gero, K. I., & Chilton, L. B. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300526 · Cited by 146

Interpretable machine learning for dementia: a systematic review

Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149

Noise Injection: Theoretical Prospects

Grandvalet, Y., Canu, S., & Boucheron, S. (1997) · Neural Computation · doi:10.1162/neco.1997.9.5.1093 · Cited by 147

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

Chen, V., Liao, Q. V., Wortman Vaughan, J., & Bansal, G. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610219 · Cited by 220

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

Manor, R., & Geva, A. B. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00146 · Cited by 144

Efficient densely connected convolutional neural networks

Li, G., Zhang, M., Li, J., Lv, F., & Tong, G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107610 · Cited by 145

An analog neural network processor with programmable topology

Boser, B. E., Sackinger, E., Bromley, J., Le Cun, Y., & Jackel, L. D. (1991) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.104196 · Cited by 142

Interpretation of deep learning in genomics and epigenomics

Talukder, A., Barham, C., Li, X., & Hu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa177 · Cited by 142

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

Xu, W., Diesen, E., He, T., Reuter, K., & Margraf, J. T. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.3c14486 · Cited by 151

Ig-VAE: Generative Modeling of Protein Structure by Direct 3D Coordinate Generation

Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2020) · doi:10.1101/2020.08.07.242347 · Cited by 141

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Yu, H., Shen, B., Ran, D., Zhang, J., Zhang, Q., Ma, Y., Liang, G., Li, Y., Wang, Q., & Xie, T. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3623316 · Cited by 140

Convolutions are competitive with transformers for protein sequence pretraining

Yang, K. K., Fusi, N., & Lu, A. X. (2024) · Cell Systems · doi:10.1016/j.cels.2024.01.008 · Cited by 137

Machine learning for perturbational single-cell omics

Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137

Harms from Increasingly Agentic Algorithmic Systems

Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., Langosco, L., He, Z., Duan, Y., Carroll, M., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594033 · Cited by 149

Machine learning with a reject option: A survey

Hendrickx, K., Perini, L., Van der Plas, D., Meert, W., & Davis, J. (2024) · Machine Learning · doi:10.1007/s10994-024-06534-x · Cited by 134

Neural probabilistic logic programming in DeepProbLog

Manhaeve, R., Dumančić, S., Kimmig, A., Demeester, T., & De Raedt, L. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103504 · Cited by 131

Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13336 · Cited by 130

Out of the BLEU: how should we assess quality of the Code Generation models?

Evtikhiev, M., Bogomolov, E., Sokolov, Y., & Bryksin, T. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111741 · Cited by 127

Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals

Elazar, Y., Ravfogel, S., Jacovi, A., & Goldberg, Y. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00359 · Cited by 126

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102102 · Cited by 126

Challenges and future directions of secure federated learning: a survey

Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127

BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125

Hurtful words: quantifying biases in clinical contextual word embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning · doi:10.1145/3368555.3384448 · Cited by 122

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141

Machine learning modeling of family wide enzyme-substrate specificity screens

Goldman, S., Das, R., Yang, K. K., & Coley, C. W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009853 · Cited by 120