355 citations · 1.6k across the 54 of their papers we have counts for
8 papers · 1 filter
Foveation-based Mechanisms Alleviate Adversarial Examples
Yan Luo, Xavier Boix, Gemma Roig +2
We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based…
How Important is Weight Symmetry in Backpropagation?
Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport probl…
Holographic Embeddings of Knowledge Graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose…
Deep Convolutional Networks are Hierarchical Kernel Machines
Fabio Anselmi, Lorenzo Rosasco, Cheston Tan +1
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 un…
Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi +2
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we deve…
Learning with Group Invariant Features: A Kernel Perspective
Youssef Mroueh, Stephen Voinea, Tomaso Poggio
We analyze in this paper a random feature map based on a theory of invariance I-theory introduced recently. More specifically, a group invariant signal signature is obtained throug…
Convex Learning of Multiple Tasks and their Structure
Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio +1
Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is…
On Invariance and Selectivity in Representation Learning
Fabio Anselmi, Lorenzo Rosasco, Tomaso Poggio
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…