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Found 14 papers, 3 papers with code
Analysis of Memory Organization for Dynamic Neural Networks
An increasing number of neural memory networks have been developed, leading to the need for a systematic approach to analyze and compare their underlying memory capabilities.
Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networks
We propose a new perspective to analyze the hidden state space based on an eigen decomposition of the weight matrix.
Universal Recurrent Event Memories for Streaming Data
In this paper, we propose a new event memory architecture (MemNet) for recurrent neural networks, which is universal for different types of time series data such as scalar, multivariate or symbolic.
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process.
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio
We seek to develop simultaneous segmentation and classification of notes from audio recordings in presence of outliers.
Uncertainty quantification for multiclass data description
MDD-KM provides uncertainty quantification and can be deployed to build classification systems for the realistic scenario where out-of-distribution (OOD) samples are present among the test data.
Modularizing Deep Learning via Pairwise Learning With Kernels
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation.
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.
Learning Backpropagation-Free Deep Architectures with Kernels
Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand.
A Taxonomy for Neural Memory Networks
The taxonomy includes all the popular memory networks: vanilla recurrent neural network (RNN), long short term memory (LSTM ), neural stack and neural Turing machine and their variants.
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation
With this method, we obtain a counterpart of any given NN that is powered by kernel machines instead of neurons.
Marine Animal Classification with Correntropy Loss Based Multi-view Learning
Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.
A novel family of non-parametric cumulative based divergences for point processes
However, these statistics do not fully describe a point process and thus the tests can be misleading.
