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1. Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
   2025/04/06 by Mazurek, Szymon, Caputa, Jakub, Argasiński, Jan K. +1 · 3 citations
   #37N25 #60J22 #68Q32 #68T05 #92B20 #92B25 #Artificial Intelligence (cs.AI) #C.1.3 #F.4.1 #FOS: Computer and information sciences #I.2.10 #I.2.3 #I.2.6 #I.2.9 #J.2 #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
2. Genomic Language Models: Opportunities and Challenges
   2024/07/16 by Benegas, Gonzalo, Ye, Chengzhong, Albors, Carlos +2 · 8 citations
   #68T07 #68T50 #92-08 #92B20 #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Machine Learning (stat.ML)
3. Fractal Basins as a Mechanism for the Nimble Brain
   2023/10/31 by Bollt, Erik, Fish, Jeremie, Kumar, Anil +2 · 1 citation
   #34C28 #37N25 #92B20 #92B25 #Chaotic Dynamics (nlin.CD) #Dynamical Systems (math.DS) #FOS: Biological sciences #FOS: Mathematics #FOS: Physical sciences #Neurons and Cognition (q-bio.NC)
4. A voltage-conductance kinetic system from neuroscience: probabilistic reformulation and exponential ergodicity
   2023/05/06 by Dou, Xu'an, Kong, Fanhao, Xu, Weijun +1 · 1 citation
   #35B40 #35Q84 #35Q92 #37A25 #92B20 #Analysis of PDEs (math.AP) #FOS: Biological sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC) #Probability (math.PR)
5. On the universal approximation property of radial basis function neural networks
   2023/04/05 by Ismayilova, Aysu, Ismayilov, Muhammad · 1 citation
   #41A30 #41A63 #68T05 #92B20 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
6. Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
   2023/02/23 by Raluca Jalaboi, Jalaboi, Raluca, Ole Winther +3 · 1 citation
   Medicine · #68T45 #92B20 #92C50 #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #Machine Learning (cs.LG)
7. Thundernna: a white box adversarial attack
   2021/11/24 by Ye, Linfeng, Hamidi, Shayan Mohajer · 1 citation
   #92B20 #FOS: Computer and information sciences #I.2.m #Machine Learning (cs.LG)
8. Interacting Hawkes processes with multiplicative inhibition
   2021/05/21 by Céline Duval, Duval, Céline, Éric Luçon +3 · 3 citations
   Biochemistry, Genetics and Molecular Biology · Mathematics · #60F99 #60G55 #92B20 #Diffusion and Search Dynamics #FOS: Biological sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC) #Point processes and geometric inequalities #Probability (math.PR)
9. Nonlocal cross-diffusion systems for multi-species populations and networks
   2021/04/13 by Jüngel, Ansgar, Portisch, Stefan, Zurek, Antoine · 1 citation
   #35K40 #35K55 #35Q92 #68T07 #92B20 #Analysis of PDEs (math.AP) #FOS: Mathematics
10. BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
    2020/06/01 by María de la Iglesia-Vayá, Vayá, Maria de la Iglesia, Jose Manuel Saborit +23 · 3 citations
    Medicine · #68T50 #92B10 #92B20 #92C50 #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
11. MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
    2020/05/30 by Zhang, Haimiao, Liu, Baodong, Yu, Hengyong +1 · 2 citations
    #65F10 #68T05 #92B20 #94A08 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Optimization and Control (math.OC) #electronic engineering #information engineering
12. Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks
    2020/02/22 by Iiduka, Hideaki · 1 citation
    #65K05 #90C25 #90C90 #92B20 #FOS: Mathematics #Optimization and Control (math.OC)
13. The Mathematical Structure of Integrated Information Theory
    2020/02/18 by Kleiner, Johannes, Tull, Sean · 2 citations
    #62B10 #81P45 #92B20 #94A15 #94A17 #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #H.1.1 #H.5.0 #Information Theory (cs.IT) #J.3 #Neurons and Cognition (q-bio.NC) #Quantum Physics (quant-ph)
14. Nonlinear Approximation and (Deep) ReLU Networks
    2019/05/05 by Daubechies, I., DeVore, R., Foucart, S. +2 · 1 citation
    #41A25 #41A30 #41A46 #68T99 #82C32 #92B20 #FOS: Computer and information sciences #Machine Learning (cs.LG)
15. Filippov flows and mean-field limits in the kinetic singular Kuramoto model
    2019/03/04 by Poyato, David · 1 citation
    #34A60 #34C15 #35B40 #35D30 #35L81 #35Q70 (Primary) 35Q83 #58J45 #92B20 #92B25 (Secondary) #Analysis of PDEs (math.AP) #FOS: Mathematics
16. The capacity of feedforward neural networks
    2019/01/02 by Baldi, Pierre, Vershynin, Roman · 2 citations
    #06E30 #68Q32 #92B20 #Combinatorics (math.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
17. Periodicity induced by noise and interaction in the kinetic mean-field FitzHugh-Nagumo model
    2018/11/01 by Luçon, Eric, Poquet, Christophe · 2 citations
    #35K55 #35Q84 #37N25 #60K35 #82C26 #82C31 #92B20 #Analysis of PDEs (math.AP) #FOS: Mathematics #Probability (math.PR)
18. Formal approaches to a definition of agents
    2017/04/10 by Biehl, Martin · 1 citation
    #92B20 #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #G.3 #H.1.1 #I.2.11 #I.5.m #Information Theory (cs.IT) #J.3 #Multiagent Systems (cs.MA)
19. Two's company, three (or more) is a simplex: Algebraic-topological tools\n for understanding higher-order structure in neural data
    2016/01/07 by Chad Giusti, Giusti, Chad, Robert Ghrist +3 · 11 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #57Q05 #92-02 #92B20 #Algebraic Topology (math.AT) #Cell Image Analysis Techniques #Data Visualization and Analytics #FOS: Biological sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #Topological and Geometric Data Analysis
20. The operad of temporal wiring diagrams: formalizing a graphical language for discrete-time processes
    2013/07/25 by Rupel, Dylan, Spivak, David I. · 1 citation
    #08A70 #18B20 #18D50 #68Q05 #91B74 #92B20 #93A13 #B.5.2 #B.7.2 #C.0 #C.1 #Category Theory (math.CT) #D.2.2 #D.2.6 #D.3.3 #F.1.1 #F.4.3 #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC) #Programming Languages (cs.PL)
21. Synchronization and random long time dynamics for mean-field plane rotators
    2012/09/20 by Lorenzo Bertini, Bertini, Lorenzo, Giambattista Giacomin +3 · 1 citation
    Physics and Astronomy · #37N25 #60K35 #82C26 #82C31 #92B20 #Adaptation and Self-Organizing Systems (nlin.AO) #Advanced Thermodynamics and Statistical Mechanics #FOS: Mathematics #FOS: Physical sciences #Mathematical Physics (math-ph) #Probability (math.PR) #Statistical Mechanics and Entropy #stochastic dynamics and bifurcation
22. Mean Field description of and propagation of chaos in recurrent multipopulation networks of Hodgkin-Huxley and Fitzhugh-Nagumo neurons
    2011/10/19 by Javier Baladron, Diego Fasoli, Baladron, Javier +5 · 1 citation
    Computer Science · Neuroscience · Physics and Astronomy · #35Q80 #60B10 #60F99 #82C32 #82C80 #92B20 #Advanced Thermodynamics and Statistical Mechanics #FOS: Biological sciences #FOS: Mathematics #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Nonlinear Dynamics and Pattern Formation #Probability (math.PR) #stochastic dynamics and bifurcation
23. Analysis of Nonlinear Noisy Integrate&Fire Neuron Models: blow-up and steady states
    2010/10/22 by Cáceres, María J., Carrillo, José A., Perthame, Benoît · 1 citation
    #35K60 #82C31 #92B20 #Analysis of PDEs (math.AP) #FOS: Biological sciences #FOS: Mathematics #Neurons and Cognition (q-bio.NC)