Low rank mechanisms underlying flexible visual representations
نویسندگان
چکیده
منابع مشابه
Learning Robust Low-Rank Representations
In this paper we present a comprehensive framework for learning robust low-rank representations by combining and extending recent ideas for learning fast sparse coding regressors with structured non-convex optimization techniques. This approach connects robust principal component analysis (RPCA) with dictionary learning techniques and allows its approximation via trainable encoders. We propose ...
متن کاملNeural Mechanisms Underlying Visual Object Recognition.
Invariant visual object recognition and the underlying neural representations are fundamental to higher-level human cognition. To understand these neural underpinnings, we combine human and monkey psychophysics, large-scale neurophysiology, neural perturbation methods, and computational modeling to construct falsifiable, predictive models that aim to fully account for the neural encoding and de...
متن کاملUnifying Low-Rank Models for Visual Learning
Many problems in signal processing, machine learning and computer vision can be solved by learning low rank models from data. In computer vision, problems such as rigid structure from motion have been formulated as an optimization over subspaces with fixed rank. These hard -rank constraints have traditionally been imposed by a factorization that parameterizes subspaces as a product of two matri...
متن کاملMechanisms Underlying the Emergence of Object Representations during Infancy
The effects of individual versus category training, using behavioral indices of stimulus discrimination and neural ERPs indices of holistic processing, were examined in infants. Following pretraining assessments at 6 months, infants were sent home with training books of objects for 3 months. One group of infants was trained with six different strollers labeled individually, and another group wa...
متن کاملImproving Multifrontal Methods by Means of Block Low-Rank Representations
Matrices coming from elliptic Partial Differential Equations (PDEs) have been shown to have a low-rank property: well defined off-diagonal blocks of their Schur complements can be approximated by low-rank products. Given a suitable ordering of the matrix which gives to the blocks a geometrical meaning, such approximations can be computed using an SVD or a rank-revealing QR factorization. The re...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 2020
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.2005797117