Search Results for author:
Found 17 papers, 8 papers with code
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
In this paper, we equip Prototypical Networks (PNs) with a novel dissimilarity measure to enable discriminative feature normalization for few-shot learning.
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances.
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Distance-based classification is frequently used in transductive few-shot learning (FSL).
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples.
The Kernelized Taylor Diagram
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
RELAX: Representation Learning Explainability
Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering.
Leveraging tensor kernels to reduce objective function mismatch in deep clustering
In this work we study OFM in deep clustering, and find that the popular autoencoder-based approach to deep clustering can lead to both reduced clustering performance, and a significant amount of OFM between the reconstruction and clustering objectives.
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
In this paper, we propose an IP analysis using the new matrix--based R\'enyi's entropy coupled with tensor kernels over convolutional layers, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data.
Information Plane Analysis of Deep Neural Networks via Matrix--Based Renyi's Entropy and Tensor Kernels
In this paper, we propose an IP analysis using the new matrix--based R\'enyi's entropy coupled with tensor kernels over convolutional layers, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data.
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.
Reservoir computing approaches for representation and classification of multivariate time series
The architectures are compared to other MTS classifiers, including deep learning models and time series kernels.
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate time series.
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data.
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space.
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series.
