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Tomaso Poggio

  1. Learning with a Wasserstein Loss
    2015/06/17 by Charlie Frogner, Frogner, Charlie, Chiyuan Zhang +7 · 46 citations
    Computer Science · Medicine · #Topological and Geometric Data Analysis #Glioma Diagnosis and Treatment #Advanced Neuroimaging Techniques and Applications
  2. Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex
    2001/01/12 by David J. Freedman, Maximilian Riesenhuber, Tomaso Poggio +1 · 1 voice · 20 citations
    Neuroscience · Psychology · #Action Observation and Synchronization #Face Recognition and Perception #Visual perception and processing mechanisms
  3. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
    2016/04/13 by Qianli Liao, Liao, Qianli, Tomaso Poggio +1 · 21 citations
    Computer Science · Neuroscience · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #Visual Attention and Saliency Detection #Visual perception and processing mechanisms
  4. A computational theory of human stereo vision
    1979/05/23 by David Marr, D. Marr, T. Poggio +1 · 8 citations
    Computer Science · Neuroscience · #Advanced Vision and Imaging #Neural dynamics and brain function #Visual perception and processing mechanisms
  5. A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks
    1994/07/01 by James M. Hutchinson, JAMES M. HUTCHINSON, ANDREW W. LO +3 · 10 citations
    Economics, Econometrics and Finance · Decision Sciences · Computer Science · #Stochastic processes and financial applications #Stock Market Forecasting Methods #Neural Networks and Applications
  6. How Important is Weight Symmetry in Backpropagation?
    2015/10/17 by Qianli Liao, Liao, Qianli, Joel Z. Leibo +3 · 12 citations
    Computer Science · Engineering · #Machine Learning and ELM #Nanopore and Nanochannel Transport Studies #Sparse and Compressive Sensing Techniques
  7. Learning Functions: When Is Deep Better Than Shallow
    2016/03/03 by H. N. Mhaskar, Qianli Liao, Mhaskar, Hrushikesh +3 · 11 citations
    Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  8. Theory of Deep Learning III: explaining the non-overfitting puzzle
    2017/12/30 by Tomaso Poggio, Kenji Kawaguchi, Poggio, Tomaso +13 · 11 citations
    Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
  9. Unsupervised Learning of Invariant Representations in Hierarchical\n Architectures
    2013/11/17 by Fabio Anselmi, Anselmi, Fabio, Joel Z. Leibo +9 · 9 citations
    Computer Science · Engineering · Neuroscience · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face Recognition and Perception #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Remote-Sensing Image Classification #Visual perception and processing mechanisms
  10. SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
    2022/06/12 by Tomer Galanti, Galanti, Tomer, Zachary S. Siegel +5 · 5 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  11. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
    2016/10/19 by Qianli Liao, Kenji Kawaguchi, Liao, Qianli +3 · 4 citations
    Computer Science · Neuroscience · #Neural Networks and Applications #Blind Source Separation Techniques #Neural dynamics and brain function
  12. Multiclass Learning with Simplex Coding
    2012/09/06 by Youssef Mroueh, Tomaso Poggio, Mroueh, Youssef +5 · 2 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  13. Learning with Group Invariant Features: A Kernel Perspective
    2015/06/08 by Youssef Mroueh, Mroueh, Youssef, Stephen Voinea +3 · 2 citations
    Computer Science · #Bayesian Methods and Mixture Models #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  14. Explicit regularization and implicit bias in deep network classifiers trained with the square loss
    2020/12/31 by Tomaso Poggio, Poggio, Tomaso, Qianli Liao +1 · 3 citations
    Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques
  15. Function approximation by deep networks
    2019/05/30 by H. N. Mhaskar, Tomaso Poggio, Mhaskar, H. N. +1 · 2 citations
    Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  16. A Surprising Linear Relationship Predicts Test Performance in Deep Networks
    2018/07/25 by Qianli Liao, Liao, Qianli, Brando Miranda +7 · 5 citations
    Computer Science · Materials Science · #Domain Adaptation and Few-Shot Learning #Adversarial Robustness in Machine Learning #Machine Learning in Materials Science
  17. Parameter Symmetry Potentially Unifies Deep Learning Theory
    2025/02/07 by Liu Ziyin, Yizhou Xu, Ziyin, Liu +5 · 4 citations
    Computer Science · #Neural Networks and Applications
  18. Formation of Representations in Neural Networks
    2024/10/03 by Liu Ziyin, Isaac L. Chuang, Ziyin, Liu +5 · 4 citations
    Computer Science · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications
  19. Double descent in the condition number
    2019/12/12 by Tomaso Poggio, Poggio, Tomaso, Gil Kur +3 · 2 citations
    Mathematics · #Advanced Combinatorial Mathematics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Random Matrices and Applications #Stochastic processes and statistical mechanics
  20. What if Eye...? Computationally Recreating Vision Evolution
    2025/01/25 by Kushagra Tiwary, Aaron Young, Aaron J. Young +16 · 1 voice · 2 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Evolutionary Algorithms and Applications #FOS: Biological sciences #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC)
  21. Learning Manifolds with K-Means and K-Flats
    2012/09/05 by Guillermo D. Cañas, Canas, Guillermo D., Tomaso Poggio +3 · 1 citation
    Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #K.3.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
  22. Theory IIIb: Generalization in Deep Networks
    2018/06/29 by Tomaso Poggio, Poggio, Tomaso, Qianli Liao +6 · 2 citations
    Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  23. Self-Assembly of a Biologically Plausible Learning Circuit
    2024/12/28 by Qianli Liao, Liao, Qianli, Liu Ziyin +8 · 3 citations
    Engineering · Biochemistry, Genetics and Molecular Biology · #Modular Robots and Swarm Intelligence #Electrowetting and Microfluidic Technologies #DNA and Biological Computing
  24. Group Invariant Deep Representations for Image Instance Retrieval
    2016/01/09 by Olivier Morère, Morère, Olivier, Antoine Veillard +9 · 2 citations
    Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR)
  25. Deep Convolutional Networks are Hierarchical Kernel Machines
    2015/08/05 by Fabio Anselmi, Anselmi, Fabio, Lorenzo Rosasco +5 · 1 citation
    Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)