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Found 9 papers, 2 papers with code
Local Search, Semantics, and Genetic Programming: a Global Analysis
In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions.
Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension
Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks.
Understanding robustness and generalization of artificial neural networks through Fourier masks
Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place.
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
Inspired by theoretical work on linear full-width convolutional models, we hypothesize that the local (i. e. bounded-width) convolutional operations commonly used in current neural networks are implicitly biased to learn high frequency features, and that this is one of the root causes of high frequency adversarial examples.
A computational model for grid maps in neural populations
Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain.
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations.
Deep Convolutional Networks are Hierarchical Kernel Machines
In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates under a group.
On Invariance and Selectivity in Representation Learning
We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other.
Unsupervised Learning of Invariant Representations in Hierarchical Architectures
It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition---and that this representation may be continuously learned in an unsupervised way during development and visual experience.
