
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
While deep learning is successful in a number of applications, it is not...
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Theory III: Dynamics and Generalization in Deep Networks
We review recent observations on the dynamical systems induced by gradie...
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Biologicallyplausible learning algorithms can scale to large datasets
The backpropagation (BP) algorithm is often thought to be biologically i...
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A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Given two networks with the same training loss on a dataset, when would ...
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Theory IIIb: Generalization in Deep Networks
A main puzzle of deep neural networks (DNNs) revolves around the apparen...
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Approximate inference with Wasserstein gradient flows
We present a novel approximate inference method for diffusion processes,...
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An analysis of training and generalization errors in shallow and deep networks
An open problem around deep networks is the apparent absence of overfit...
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Theory of Deep Learning IIb: Optimization Properties of SGD
In Theory IIb we characterize with a mix of theory and experiments the o...
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Theory of Deep Learning III: explaining the nonoverfitting puzzle
A main puzzle of deep networks revolves around the absence of overfittin...
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FisherRao Metric, Geometry, and Complexity of Neural Networks
We study the relationship between geometry and capacity measures for dee...
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Pruning Convolutional Neural Networks for Image Instance Retrieval
In this work, we focus on the problem of image instance retrieval with d...
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Do Deep Neural Networks Suffer from Crowding?
Crowding is a visual effect suffered by humans, in which an object that ...
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Theory II: Landscape of the Empirical Risk in Deep Learning
Previous theoretical work on deep learning and neural network optimizati...
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Compression of Deep Neural Networks for Image Instance Retrieval
Image instance retrieval is the problem of retrieving images from a data...
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Streaming Normalization: Towards Simpler and More Biologicallyplausible Normalizations for Online and Recurrent Learning
We systematically explored a spectrum of normalization algorithms relate...
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Viewtolerant face recognition and Hebbian learning imply mirrorsymmetric neural tuning to head orientation
The primate brain contains a hierarchy of visual areas, dubbed the ventr...
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
We discuss relations between Residual Networks (ResNet), Recurrent Neura...
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Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
The goal of this work is the computation of very compact binary hashes f...
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Group Invariant Deep Representations for Image Instance Retrieval
Most image instance retrieval pipelines are based on comparison of vecto...
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Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versat...
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Deep Convolutional Networks are Hierarchical Kernel Machines
In itheory a typical layer of a hierarchical architecture consists of H...
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Learning with a Wasserstein Loss
Learning to predict multilabel outputs is challenging, but in many prob...
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Learning with Group Invariant Features: A Kernel Perspective
We analyze in this paper a random feature map based on a theory of invar...
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Unsupervised learning of clutterresistant visual representations from natural videos
Populations of neurons in inferotemporal cortex (IT) maintain an explici...
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Neural tuning size is a key factor underlying holistic face processing
Faces are a class of visual stimuli with unique significance, for a vari...
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A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms simi...
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Unsupervised Learning of Invariant Representations in Hierarchical Architectures
The present phase of Machine Learning is characterized by supervised lea...
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Can a biologicallyplausible hierarchy effectively replace face detection, alignment, and recognition pipelines?
The standard approach to unconstrained face recognition in natural photo...
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On Learnability, Complexity and Stability
We consider the fundamental question of learnability of a hypotheses cla...
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Multiclass Learning with Simplex Coding
In this paper we discuss a novel framework for multiclass learning, defi...
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Learning Manifolds with KMeans and KFlats
We study the problem of estimating a manifold from random samples. In pa...
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