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Found 10 papers, 2 papers with code
CA-Stream: Attention-based pooling for interpretable image recognition
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map.
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.
Towards the generation of synchronized and believable non-verbal facial behaviors of a talking virtual agent
This paper introduces a new model to generate rhythmically relevant non-verbal facial behaviors for virtual agents while they speak.
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.
An AI-powered blood test to detect cancer using nanoDSF
We describe a novel cancer diagnostic method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry.
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs.
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.
Majority Vote of Diverse Classifiers for Late Fusion
Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse.
