The DOI matches its Crossref record.
Proceedings of the 24th international conference on Machine learning
Abstract
This volume contains the papers accepted to the 24th International Conference on Machine Learning (ICML 2007), which was held at Oregon State University in Corvalis, Oregon, from June 20th to 24th, 2007. ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and discussion of current research in the field of machine learning. These proceedings can also be found online at: http://www.machinelearning.org. This year there were 522 submissions to ICML. There was a very thorough review process, in which each paper was reviewed by three program committee (PC) members. Authors were able to respond to the initial reviews, and the PC members could then modify their reviews based on online discussions and the content of this author response. For the first time this year there were two discussion periods led by the senior program committee (SPC), one just before and one after the submission of author responses. At the end of the second discussion period, the SPC members gave their recommendations and provided a summary review for each of their papers. Also for the first time, authors were asked to submit a list of changes with their final accepted papers, which was checked by the SPCs to ensure that reviewer comments had been addressed. Apart from the length restrictions on papers and the compressed time frame, the review process for ICML resembles that of many journal publications. In total, 150 papers were accepted to ICML this year, including a very small number of papers which were initially conditionally accepted, yielding an overall acceptance rate of 29%. ICML attracts submissions from machine learning researchers around the globe. The 150 accepted papers this year were geographically distributed as follows: 66 papers had a first author from the US, 32 from Europe, 19 from China or Hong Kong, 11 from Canada, 6 from India, 5 each from Australia and Japan, 3 from Israel, and 1 each from Korea, Russia and Taiwan. In addition to the main program of accepted papers, which includes both a talk and poster presentation for each paper, the ICML program included 3 workshops and 8 tutorials on machine learning topics which are currently of broad interest. We were also extremely pleased to have David Heckerman (Microsoft Research), Joshua Tenenbaum (Massachussetts Institute of Technology), and Bernhard Schölkopf (Max Planck Institute for Biological Cybernetics) as the invited speakers this year. Thanks to sponsorship by the Machine Learning Journal, we were able to award a number of outstanding student paper prizes. We were fortunate this year that ICML was co-located with the International Conference on Inductive Logic Programming (ILP 2007). ICML and ILP held joint sessions on the first day of ICML 2007.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 101–200 of 264
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0
O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611477 · Cited by 210
Computational Health Informatics in the Big Data Age: A Survey
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210
Generative AI design for building structures
Liao, W., Lu, X., Fei, Y., Gu, Y., & Huang, Y. (2024) · Automation in Construction · doi:10.1016/j.autcon.2023.105187 · Cited by 213
Guidelines and evaluation of clinical explainable AI in medical image analysis
Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102684 · Cited by 199
Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445935 · Cited by 195
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195
Integrating multi-omics data through deep learning for accurate cancer prognosis prediction
Chai, H., Zhou, X., Zhang, Z., Rao, J., Zhao, H., & Yang, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104481 · Cited by 194
Giovanola, B., & Tiribelli, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01455-6 · Cited by 193
Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., et al. (2021) · Radiology · doi:10.1148/radiol.2020203511 · Cited by 190
Large Language Models for Cyber Security: A Systematic Literature Review
Xu, H., Wang, S., Li, N., Wang, K., Zhao, Y., Chen, K., Yu, T., Liu, Y., & Wang, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3769676 · Cited by 189
Deep learning-enabled anomaly detection for IoT systems
Abusitta, A., de Carvalho, G. H. S., Wahab, O. A., Halabi, T., Fung, B. C. M., & Mamoori, S. A. (2023) · Internet of Things · doi:10.1016/j.iot.2022.100656 · Cited by 184
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · ACM SIGPLAN Notices · doi:10.1145/3093336.3037698 · Cited by 185
Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., et al. (2021) · Nature Cancer · doi:10.1038/s43018-020-00169-2 · Cited by 180
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.117899 · Cited by 180
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
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164
Zhou, N.-R., Zhang, T.-F., Xie, X.-W., & Wu, J.-Y. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116891 · Cited by 161
Loss of plasticity in deep continual learning
Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172
Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155
Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools
Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025) · Journal of Empirical Legal Studies · doi:10.1111/jels.12413 · Cited by 182
Tracing the evolution of AI in the past decade and forecasting the emerging trends
Shao, Z., Zhao, R., Yuan, S., Ding, M., & Wang, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118221 · Cited by 153
Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., & Schiratti, J.-B. (2023) · doi:10.1101/2023.07.21.23292757 · Cited by 148
It's complicated: the relationship between user trust, model accuracy and explanations in AI
Papenmeier, A., Kern, D., Englebienne, G., & Seifert, C. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3495013 · Cited by 144
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143
Model agnostic generation of counterfactual explanations for molecules
Wellawatte, G. P., Seshadri, A., & White, A. D. (2022) · Chemical Science · doi:10.1039/d1sc05259d · Cited by 143
Jamming transition as a paradigm to understand the loss landscape of deep neural networks
Geiger, M., Spigler, S., d'Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., & Wyart, M. (2019) · Physical Review E · doi:10.1103/physreve.100.012115 · Cited by 143
Information decomposition and the informational architecture of the brain
Luppi, A. I., Rosas, F. E., Mediano, P. A. M., Menon, D. K., & Stamatakis, E. A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.11.005 · Cited by 140
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
You, H., Sun, Z., Shi, H., Yu, Z., Zhao, Y., Zhang, Y., Li, C., Li, B., & Lin, Y. (2023) · 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10071027 · Cited by 138
Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Al Sayed, K., Boodi, A., Sadeghian Broujeny, R., & Beddiar, K. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110085 · Cited by 140
Artificial intelligence and the value of transparency
Walmsley, J. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01066-z · Cited by 131
ILP turns 20: Biography and future challenges
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126
Meta-interpretive learning: application to grammatical inference
Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A. (2014) · Machine Learning · doi:10.1007/s10994-013-5358-3 · Cited by 120
The life cycle of large language models in education: A framework for understanding sources of bias
Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13505 · Cited by 119
Automobile insurance classification ratemaking based on telematics driving data
Huang, Y., & Meng, S. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113156 · Cited by 119
Long short-term memory-based deep recurrent neural networks for target tracking
Gao, C., Yan, J., Zhou, S., Varshney, P. K., & Liu, H. (2019) · Information Sciences · doi:10.1016/j.ins.2019.06.039 · Cited by 118
Airline itinerary choice modeling using machine learning
Lhéritier, A., Bocamazo, M., Delahaye, T., & Acuna-Agost, R. (2019) · Journal of Choice Modelling · doi:10.1016/j.jocm.2018.02.002 · Cited by 114
Accurate prediction of cell type-specific transcription factor binding
Keilwagen, J., Posch, S., & Grau, J. (2019) · Genome Biology · doi:10.1186/s13059-018-1614-y · Cited by 112
SECNLP: A survey of embeddings in clinical natural language processing
Kalyan, K. S., & Sangeetha, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103323 · Cited by 111
Khader, F., Müller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Haarburger, C., Stegmaier, J., Bressem, K., Kuhl, C., Nebelung, S., et al. (2023) · Radiology · doi:10.1148/radiol.230806 · Cited by 107
Rethink reporting of evaluation results in AI
Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., et al. (2023) · Science · doi:10.1126/science.adf6369 · Cited by 104
M3Care: Learning with Missing Modalities in Multimodal Healthcare Data
Zhang, C., Chu, X., Ma, L., Zhu, Y., Wang, Y., Wang, J., & Zhao, J. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539388 · Cited by 102
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model
Jiang, Y., Zhang, G., You, J., Zhang, H., Yao, R., Xie, H., Zhang, L., Xia, Z., Dai, M., Wu, Y., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00808-8 · Cited by 97
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96
Al-Busaidi, A. S., Raman, R., Hughes, L., Albashrawi, M. A., Malik, T., Dwivedi, Y. K., Al- Alawi, T., AlRizeiqi, M., Davies, G., Fenwick, M., et al. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100630 · Cited by 95
Wang, Q.-A., Zhang, C., Ma, Z.-G., & Ni, Y.-Q. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2021.113554 · Cited by 95
An integrative deep learning framework for classifying molecular subtypes of breast cancer
Mohaiminul Islam, M., Huang, S., Ajwad, R., Chi, C., Wang, Y., & Hu, P. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.005 · Cited by 90
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
A survey on online feature selection with streaming features
Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88
Huang, Z., Sheng, Z., Ma, C., & Chen, S. (2024) · Communications in Transportation Research · doi:10.1016/j.commtr.2024.100127 · Cited by 88
McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.003 · Cited by 86
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
Pahud de Mortanges, A., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Pöllinger, A., & Reyes, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01190-w · Cited by 93
DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT
Miao, Q., Zheng, W., Lv, Y., Huang, M., Ding, W., & Wang, F.-Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123561 · Cited by 78
Tigre Moura, F., Castrucci, C., & Hindley, C. (2023) · The Journal of Creative Behavior · doi:10.1002/jocb.600 · Cited by 79
Targeted scoring functions for virtual screening.
Seifert, M. H. J. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.03.013 · Cited by 76
Structured sampling of olfactory input by the fly mushroom body
Zheng, Z., Li, F., Fisher, C., Ali, I. J., Sharifi, N., Calle-Schuler, S., Hsu, J., Masoodpanah, N., Kmecova, L., Kazimiers, T., et al. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.031 · Cited by 75
Hu, J., Small, H., Kean, H., Takahashi, A., Zekelman, L., Kleinman, D., Ryan, E., Nieto-Castañón, A., Ferreira, V., & Fedorenko, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac350 · Cited by 73
Meta‐analysis and aggregation of multiple published prediction models
Debray, T. P. A., Koffijberg, H., Nieboer, D., Vergouwe, Y., Steyerberg, E. W., & Moons, K. G. M. (2014) · Statistics in Medicine · doi:10.1002/sim.6080 · Cited by 73
Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration
Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00494 · Cited by 72
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
Reducing conversational agents' overconfidence through linguistic calibration
Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2020) · arXiv · doi:10.48550/arxiv.2012.14983 · Cited by 72
Optimizing quantum heuristics with meta-learning
Wilson, M., Stromswold, R., Wudarski, F., Hadfield, S., Tubman, N. M., & Rieffel, E. G. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00022-w · Cited by 79
Balch, J. K., Iglesias, V., Braswell, A. E., Rossi, M. W., Joseph, M. B., Mahood, A. L., Shrum, T. R., White, C. T., Scholl, V. M., McGuire, B., et al. (2020) · Earth's Future · doi:10.1029/2019ef001319 · Cited by 71
Explainable machine learning for public policy: Use cases, gaps, and research directions
Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73
Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Badri, N., Kboubi, F., & Chaibi, A. H. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.132 · Cited by 65
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations
Hu, J., Gao, J., Fang, X., Liu, Z., Wang, F., Huang, W., Wu, H., & Zhao, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac302 · Cited by 65
Lehr, S. A., Caliskan, A., Liyanage, S., & Banaji, M. R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404328121 · Cited by 66
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
A scoping review of fair machine learning techniques when using real-world data
Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., & Bian, J. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104622 · Cited by 61
Know Your Limits: A Survey of Abstention in Large Language Models
Wen, B., Yao, J., Feng, S., Xu, C., Tsvetkov, Y., Howe, B., & Wang, L. L. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00754 · Cited by 56
Chen, W., Gao, Y., Gao, L., & Li, X. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.03.264 · Cited by 56
Probabilistic simulation of quantum circuits using a deep-learning architecture
Carrasquilla, J., Luo, D., Pérez, F., Milsted, A., Clark, B. K., Volkovs, M., & Aolita, L. (2021) · Physical Review A · doi:10.1103/physreva.104.032610 · Cited by 53
Does ChatGPT Write Like a Student? Engagement Markers in Argumentative Essays
Jiang, F. (., & Hyland, K. (2025) · Written Communication · doi:10.1177/07410883251328311 · Cited by 52
Testing AI on language comprehension tasks reveals insensitivity to underlying meaning
Dentella, V., Günther, F., Murphy, E., Marcus, G., & Leivada, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-79531-8 · Cited by 54
Zeng, W., Dou, Y., Pan, L., Xu, L., & Peng, S. (2024) · Nature Communications · doi:10.1038/s41467-024-52293-7 · Cited by 49
Real-Time Machine Learning: The Missing Pieces
Nishihara, R., Moritz, P., Wang, S., Tumanov, A., Paul, W., Schleier-Smith, J., Liaw, R., Niknami, M., Jordan, M. I., & Stoica, I. (2017) · Proceedings of the 16th Workshop on Hot Topics in Operating Systems · doi:10.1145/3102980.3102998 · Cited by 49
On Decoding Strategies for Neural Text Generators
Wiher, G., Meister, C., & Cotterell, R. (2022) · arXiv · doi:10.48550/arxiv.2203.15721 · Cited by 48
Behavioural Cloning: Phenomena, Results and Problems
Bratko, I., Urbančič, T., & Sammut, C. (1995) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)46716-4 · Cited by 46
A remote speech-based AI system to screen for early Alzheimer's disease via smartphones
Fristed, E., Skirrow, C., Meszaros, M., Lenain, R., Meepegama, U., Cappa, S., Aarsland, D., & Weston, J. (2022) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12366 · Cited by 47
Guan, X., & Burton, H. (2022) · Structures · doi:10.1016/j.istruc.2022.10.004 · Cited by 47
A Deep Dive into Single-Cell RNA Sequencing Foundation Models
Boiarsky, R., Singh, N., Buendia, A., Getz, G., & Sontag, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.19.563100 · Cited by 43
The AI Future of Emergency Medicine
Petrella, R. J. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.031 · Cited by 43
Beguš, G. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.017 · Cited by 42
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
Rodríguez-Barroso, N., Martínez-Cámara, E., Luzón, M. V., & Herrera, F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108588 · Cited by 38
Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks
Storey-Fisher, K., Huertas-Company, M., Ramachandra, N., Lanusse, F., Leauthaud, A., Luo, Y., Huang, S., & Prochaska, J. X. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2589 · Cited by 38
Protein representations: Encoding biological information for machine learning in biocatalysis
Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38
Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37
MELTing Point: Mobile Evaluation of Language Transformers
Laskaridis, S., Katevas, K., Minto, L., & Haddadi, H. (2024) · Proceedings of the 30th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3636534.3690668 · 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
Model-based feature construction for multivariate decoding
Brodersen, K. H., Haiss, F., Ong, C. S., Jung, F., Tittgemeyer, M., Buhmann, J. M., Weber, B., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.036 · Cited by 36
Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering
Minot, M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2023.12.003 · Cited by 35
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions
Chen, K., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab577 · Cited by 33
Pfohl, S., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J., & Shah, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533166 · Cited by 30
Missing-modality enabled multi-modal fusion architecture for medical data
Wang, M., Fan, S., Li, Y., Xie, Z., & Chen, H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104796 · Cited by 31
Red Teaming Language Model Detectors with Language Models
Shi, Z., Wang, Y., Yin, F., Chen, X., Chang, K.-W., & Hsieh, C.-J. (2023) · arXiv · doi:10.48550/arxiv.2305.19713 · Cited by 30
Privacy preserving and secure robust federated learning: A survey
Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.8084 · Cited by 29
Open data and algorithms for open science in AI-driven molecular informatics
Brinkhaus, H. O., Rajan, K., Schaub, J., Zielesny, A., & Steinbeck, C. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102542 · Cited by 29
Model poisoning attacks against distributed machine learning systems
Tomsett, R., Chan, K. S., & Chakraborty, S. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2520275 · Cited by 27
Metadata
- Publisher
- ACM
- Published
- 2007-06-20
- OpenAlex
- W1583837637
- Cited by
- 11,630
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
