Jim Mutch

Projects

The computational role of eccentricity dependent resolution in the retina: consequences for hierarchical models of object recognition

CBMM Publications

J. Mutch, Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T., “Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex”, in Computational and Cognitive Neuroscience of Vision, Springer, 2017, pp. 85-104.
F. Anselmi, Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., and Poggio, T., “Unsupervised learning of invariant representations”, Theoretical Computer Science, 2015.
F. Anselmi, Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., and Poggio, T., “Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?”. 2014.
T. Poggio, Mutch, J., and Isik, L., “Computational role of eccentricity dependent cortical magnification.”. 2014.
F. Anselmi, Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., and Poggio, T., “Unsupervised Learning of Invariant Representations in Hierarchical Architectures.”. 2013.
J. Mutch and Turaga, S., “cnpkg: 3-D Convolutional Network Package for CNS”. 2012.
J. Mutch, “HMAX Package for CNS”. 2012.
J. Mutch, “hmin: A Minimal HMAX Implementation”. 2010.
J. Mutch, Knoblich, U., and Poggio, T., “CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks”. 2010.