Search Results for author:
Found 9 papers, 2 papers with code
DP-Net: Learning Discriminative Parts for image recognition
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
We study the implicit regularization effects of deep learning in tensor factorization.
Implicit Regularization in Deep Tensor Factorization
Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed.
Mapping individual differences in cortical architecture using multi-view representation learning
In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable.
Deep Networks with Adaptive Nyström Approximation
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches.
Unsupervised Object Segmentation by Redrawing
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks.
Multi-View Data Generation Without View Supervision
We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object.
Sequential Cost-Sensitive Feature Acquisition
We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost.
Robust Bloom Filters for Large MultiLabel Classification Tasks
This paper presents an approach to multilabel classification (MLC) with a large number of labels.
