Advances in neural information processing systems 7

DTATrans: Leveraging Dynamic Token-Based Quantization With Accuracy Compensation Mechanism for Efficient Transformer Architecture

Yang, T., Ma, F., Li, X., Liu, F., Zhao, Y., He, Z., & Jiang, L. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3181541 · Cited by 33

Challenges and opportunities in applied machine learning

Brodley, C. E., Rebbapragada, U., Small, K., & Wallace, B. C. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2367 · Cited by 32

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning

Hoque, R., Balakrishna, A., Putterman, C., Luo, M., Brown, D. S., Seita, D., Thananjeyan, B., Novoseller, E., & Goldberg, K. (2021) · 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551469 · Cited by 31

BiViT: Extremely Compressed Binary Vision Transformers

He, Y., Lou, Z., Zhang, L., Liu, J., Wu, W., Zhou, H., & Zhuang, B. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00520 · Cited by 31

Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services

Krissaane, I., De Niz, C., Gutiérrez-Sacristán, A., Korodi, G., Ede, N., Kumar, R., Lyons, J., Manrai, A., Patel, C., Kohane, I., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa068 · Cited by 28

Valid Model-Free Spatial Prediction

Mao, H., Martin, R., & Reich, B. J. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2147531 · Cited by 28

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference

Taniguchi, T. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1353870 · Cited by 28

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

Sun, K., Roy, A., & Tobin, J. M. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154792 · Cited by 28

Differentially private self-normalizing neural networks for adversarial robustness in federated learning

Ibitoye, O., Shafiq, M. O., & Matrawy, A. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102631 · Cited by 27

The IBM natural conversation framework: a new paradigm for conversational UX design

Moore, R. J., An, S., & Ren, G.-J. (2023) · Human–Computer Interaction · doi:10.1080/07370024.2022.2081571 · Cited by 26

DeepSS2GO: protein function prediction from secondary structure

Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26

Uncovering instabilities in variational-quantum deep Q-networks

Franz, M., Wolf, L., Periyasamy, M., Ufrecht, C., Scherer, D. D., Plinge, A., Mutschler, C., & Mauerer, W. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.08.021 · Cited by 26

See to touch: Learning tactile dexterity through visual incentives

Guzey, I., Dai, Y., Evans, B., Chintala, S., & Pinto, L. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611407 · Cited by 26

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Döppel, F. A., & Votsmeier, M. (2023) · Reaction Chemistry & Engineering · doi:10.1039/d3re00212h · Cited by 24

Large language models help facilitate the automated synthesis of information on potential pest controllers

Scheepens, D., Millard, J., Farrell, M., & Newbold, T. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14341 · Cited by 24

A theoretical account of cue averaging in the rodent head direction system

Page, H. J. I., Walters, D. M., Knight, R., Piette, C. E., Jeffery, K. J., & Stringer, S. M. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0283 · Cited by 21

Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression

Wang, Y., Beck, D., Baldwin, T., & Verspoor, K. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00483 · Cited by 23

Split and merge em algorithm for improving gaussian mixture density estimates

Ueda, N., Nakano, R., Ghahramani, Z., & Hinton, G. E. (2002) · Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378) · doi:10.1109/nnsp.1998.710657 · Cited by 23

The Parallelism Tradeoff: Limitations of Log-Precision Transformers

Merrill, W., & Sabharwal, A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00562 · Cited by 22

Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection

Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22

Vision Transformers: From Semantic Segmentation to Dense Prediction

Zhang, L., Lu, J., Zheng, S., Zhao, X., Zhu, X., Fu, Y., Xiang, T., Feng, J., & Torr, P. H. S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02173-w · Cited by 23

Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction

MacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21

DRAGON: Decentralized Fault Tolerance in Edge Federations

Tuli, S., Casale, G., & Jennings, N. R. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3199886 · Cited by 21

Online discovery of Heart Rate Variability patterns in mobile healthcare services

Vu, T. H. N., Park, N., Lee, Y. K., Lee, Y., Lee, J. Y., & Ryu, K. H. (2010) · Journal of Systems and Software · doi:10.1016/j.jss.2010.05.074 · Cited by 21

Fine-Grained Multimodal DeepFake Classification via Heterogeneous Graphs

Yin, Q., Lu, W., Cao, X., Luo, X., Zhou, Y., & Huang, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02128-1 · Cited by 23

Hallucinating structure-conditioned antibody libraries for target-specific binders

Mahajan, S. P., Ruffolo, J. A., Frick, R., & Gray, J. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.999034 · Cited by 20

Untitled work

(2022) · doi:10.2139/ssrn.4496380 · Cited by 20

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

Lekeufack, J., Angelopoulos, A. N., Bajcsy, A., Jordan, M. I., & Malik, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610041 · Cited by 18

Invisible to Machines: Designing AI that Supports Vision Work in Radiology

Anichini, G., Natali, C., & Cabitza, F. (2024) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-024-09491-0 · Cited by 18

Deepfake Speech Detection: A Spectrogram Analysis

Firc, A., Malinka, K., & Hanáček, P. (2024) · Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing · doi:10.1145/3605098.3635911 · Cited by 16

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Dutta, A., Alcaraz, J., TehraniJamsaz, A., Cesar, E., Sikora, A., & Jannesari, A. (2023) · Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing · doi:10.1145/3588195.3592984 · Cited by 16

Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Matsumoto, T., Walston, S. L., Walston, M., Kabata, D., Miki, Y., Shiba, M., & Ueda, D. (2022) · Journal of Digital Imaging · doi:10.1007/s10278-022-00691-y · Cited by 15

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

Gender and sex bias in COVID-19 epidemiological data through the lens of causality

Díaz-Rodríguez, N., Binkytė, R., Bakkali, W., Bookseller, S., Tubaro, P., Bacevičius, A., Zhioua, S., & Chatila, R. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103276 · Cited by 15

Automatic Detection of Bot-generated Tweets

Tourille, J., Sow, B., & Popescu, A. (2022) · Proceedings of the 1st International Workshop on Multimedia AI against Disinformation · doi:10.1145/3512732.3533584 · Cited by 15

Learning to summarize and answer questions about a virtual robot’s past actions

DeChant, C., Akinola, I., & Bauer, D. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10134-4 · Cited by 14

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception

Brucklacher, M., Bohté, S. M., Mejias, J. F., & Pennartz, C. M. A. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1207361 · Cited by 15

Knot or not? Identifying unknotted proteins in knotted families with sequence‐based Machine Learning model

Sikora, M., Klimentova, E., Uchal, D., Sramkova, D., Perlinska, A. P., Nguyen, M. L., Korpacz, M., Malinowska, R., Nowakowski, S., Rubach, P., et al. (2024) · Protein Science · doi:10.1002/pro.4998 · Cited by 13

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., Doerig, A., Linton, P., Konkle, T., van Gerven, M., et al. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23001553 · Cited by 12

Stochastic Remembering and Distributed Mnemonic Agency: Recalling Twentieth Century Activists with ChatGPT

Smit, R., Smits, T., & Merrill, S. (2024) · Memory Studies Review · doi:10.1163/29498902-202400015 · Cited by 12

LLexus: an AI agent system for incident management

Las-Casas, P., Kumbhare, A. G., Fonseca, R., & Agarwal, S. (2024) · ACM SIGOPS Operating Systems Review · doi:10.1145/3689051.3689056 · Cited by 14

Adaptive control of reaction–diffusion PDEs via neural operator-approximated gain kernels

Bhan, L., Shi, Y., & Krstic, M. (2025) · Systems & Control Letters · doi:10.1016/j.sysconle.2024.105968 · Cited by 12

Monitoring the role of site chemistry on the formation energy of perovskites via deep learning analysis of Hirshfeld surfaces

Williams, L., Mukherjee, A., Dasgupta, A., & Rajan, K. (2021) · Journal of Materials Chemistry C · doi:10.1039/d1tc01972d · Cited by 11

Detecting Online Grooming By Simple Contrastive Chat Embeddings

Rezaee Borj, P., Raja, K., & Bours, P. (2023) · Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics · doi:10.1145/3579987.3586564 · Cited by 11

lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures

Tarafder, S., & Bhattacharya, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01621 · Cited by 11

Generative Artificial Intelligence for Industry: Opportunities, Challenges, and Impact

Saha, B. K. (2024) · 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463245 · Cited by 11

Fine-tuning neural network quantum states

Rende, R., Goldt, S., Becca, F., & Viteritti, L. L. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043280 · Cited by 10

Leveraging effectiveness and efficiency in Page Stream Deep Segmentation

Braz, F. A., da Silva, N. C., & Lima, J. A. S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104394 · Cited by 10

Visual information processing through the interplay between fine and coarse signal pathways

Zou, X., Ji, Z., Zhang, T., Huang, T., & Wu, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.048 · Cited by 9

Revisiting Training-free NAS Metrics: An Efficient Training-based Method

Yang, T., Yang, L., Jin, X., & Chen, C. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00473 · Cited by 9

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

Lancaster, P., Hansen, N., Rajeswaran, A., & Kumar, V. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611121 · Cited by 8

Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity

(2023) · doi:10.1093/braincomms/fcae007 · Cited by 8

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning

Godinez, W. J., Trifonov, V., Fang, B., Kuzu, G., Pei, L., Guiguemde, W. A., Martin, E. J., King, F. J., Jenkins, J. L., & Skewes-Cox, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01855 · Cited by 7

Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Ortiz Castelló, V., del Tejo Catalá, O., Salvador Igual, I., & Perez-Cortes, J.-C. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420929175 · Cited by 7

Object-Centric Scene Representations Using Active Inference

Van de Maele, T., Verbelen, T., Mazzaglia, P., Ferraro, S., & Dhoedt, B. (2024) · Neural Computation · doi:10.1162/neco_a_01637 · Cited by 5

Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task

Valencia, J. D., & Hendrix, D. A. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011526 · Cited by 6

TBAL: Two-stage batch-mode active learning for image classification

Shen, Y., Song, Y., Wu, C.-H., & Kuo, C.-C. J. (2022) · Signal Processing: Image Communication · doi:10.1016/j.image.2022.116731 · Cited by 6

Cultural evolution via iterated learning and communication explains efficient color naming systems

Carlsson, E., Dubhashi, D., & Regier, T. (2023) · arXiv · doi:10.48550/arxiv.2305.10154 · Cited by 6

FINCH: Prompt-guided Key-Value Cache Compression for Large Language Models

Corallo, G., & Papotti, P. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00716 · Cited by 5

Exploring Bitslicing Architectures for Enabling FHE-Assisted Machine Learning

Sinha, S., Saha, S., Alam, M., Agarwal, V., Chatterjee, A., Mishra, A., Khazanchi, D., & Mukhopadhyay, D. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3204909 · Cited by 4

Classification of galaxy morphology based on FPN-ViT model

Jie, C., Ting-ting, X., Yu-he, D., Guang-ping, L., Xian-jun, G., Ming-cun, Y., Zhi-jing, L., & Wei-hong, Z. (2024) · Chinese Astronomy and Astrophysics · doi:10.1016/j.chinastron.2024.11.001 · Cited by 3

Effective theory of collective deep learning

Arola-Fernández, L., & Lacasa, L. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.l042040 · Cited by 3

A biologically inspired architecture with switching units can learn to generalize across backgrounds

Voina, D., Shea-Brown, E., & Mihalas, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.014 · Cited by 2

Untitled work

(1996) · doi:10.1002/(sici)1098-1063(1996)6:2<149::aid-hipo6>3.0.co;2-k