Search Results for author:
Found 15 papers, 2 papers with code
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts
Furthermore, when it comes to generating images of faces and ocular images, our approach showcases substantial enhancements with FID improvements of $1. 69$ and $0. 87$ respectively, as compared to GMM sampling, as evidenced on the CelebA and MOBIUS datasets.
Simplex Autoencoders
In this work, we propose a new approach that models the latent space of an Autoencoder as a simplex, allowing for a novel heuristic for determining the number of components in the mixture model.
Pattern Recognition Experiments on Mathematical Expressions
We provide the results of pattern recognition experiments on mathematical expressions.
A forensic analysis of the Google Home: repairing compressed data without error correction
This paper provides a detailed explanation of the steps taken to extract and repair a Google Home's internal data.
FedControl: When Control Theory Meets Federated Learning
To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters.
Federated Learning Aggregation: New Robust Algorithms with Guarantees
The resulting model is then redistributed to clients for further training.
Noise-Resilient Ensemble Learning using Evidence Accumulation Clustering
Ensemble learning methods are naturally resilient to the absence of several peers thanks to the ensemble redundancy.
Tagged Documents Co-Clustering
Tags are short sequences of words allowing to describe textual and non-texual resources such as as music, image or book.
Genealogical Population-Based Training for Hyperparameter Optimization
HyperParameter Optimization (HPO) aims at finding the best HyperParameters (HPs) of learning models, such as neural networks, in the fastest and most efficient way possible.
Index $t$-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings
$t$-SNE is an embedding method that the data science community has widely Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space.
Generating Local Maps of Science using Deep Bibliographic Coupling
Bibliographic and co-citation coupling are two analytical methods widely used to measure the degree of similarity between scientific papers.
Co-Embedding: Discovering Communities on Bipartite Graphs through Projection
Many datasets take the form of a bipartite graph where two types of nodes are connected by relationships, like the movies watched by a user or the tags associated with a file.
Optimal Covid-19 Pool Testing with a priori Information
This paper describes how to optimally detect infected patients in pools, i. e. using a minimal number of tests to precisely identify them, given the a priori probabilities that each of the patients is healthy.
ARMv8 Shellcodes from 'A' to 'Z'
We describe a methodology to automatically turn arbitrary ARMv8 programs into alphanumeric executable polymorphic shellcodes.
Cryptography and Security
New Algorithmic Approaches to Point Constellation Recognition
Whilst useful in many contexts, this work is mainly motivated by fingerprint matching.
