Search Results for author:
Found 19 papers, 6 papers with code
Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations
A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage.
SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features
One concern is that, although there are studies that give good results for these data, the data from these regions may not be sufficiently representative to describe all the traffic patterns in the rest of the world.
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agent has no prior knowledge of class boundaries or task change information.
New metrics for analyzing continual learners
This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones.
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning
We propose to use the angular Gaussian distribution, which corresponds to a Gaussian projected on the unit-sphere and derive the associated loss function.
Contrastive Learning for Online Semi-Supervised General Continual Learning
We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data.
FGOT: Graph Distances based on Filters and Optimal Transport
We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses the problem of analyzing multi-channel time series data %.
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems
This work proposes a supervised multi-channel time-series learning framework for financial stock trading.
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
This work proposes an unsupervised fusion framework based on deep convolutional transform learning.
Deep Convolutional Transform Learning -- Extended version
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL).
Wasserstein-based Graph Alignment
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices.
GOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challenging problem of comparing graphs.
Ultrametric Fitting by Gradient Descent
We study the problem of fitting an ultrametric distance to a dissimilarity graph in the context of hierarchical cluster analysis.
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
In this paper, we propose a scalable algorithm for spectral embedding.
OrthoNet: Multilayer Network Data Clustering
We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space.
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
In this paper, we develop a novel second-order method for training feed-forward neural nets.
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method.
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image.
