26 citations · 55 across the 8 of their papers we have counts for
9 papers
A Method to Predict Semantic Relations on Artificial Intelligence Papers
Francisco Andrades, Ricardo Ñanculef
Predicting the emergence of links in large evolving networks is a difficult task with many practical applications. Recently, the Science4cast competition has illustrated this chall…
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Ricardo Ñanculef, Francisco Mena, Antonio Macaluso +2
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient…
Evaluating Bregman Divergences for Probability Learning from Crowd
F. A. Mena, R. Ñanculef
The crowdsourcing scenarios are a good example of having a probability distribution over some categories showing what the people in a global perspective thinks. Learn a predictive…
Efficient Classification of Multi-Labelled Text Streams by Clashing
Ricardo Ñanculef, Ilias Flaounas, Nello Cristianini
We present a method for the classification of multi-labelled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence o…
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Emanuele Frandi, Ricardo Nanculef, Stefano Lodi +2
Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning. T…
A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification
Emanuele Frandi, Ricardo Nanculef, Johan A. K. Suykens
Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choic…
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning
Emanuele Frandi, Ricardo Nanculef, Johan Suykens
Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a s…
A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale SVM Training
Hector Allende, Emanuele Frandi, Ricardo Nanculef +1
Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-W…
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Emanuele Frandi, Ricardo Nanculef, Maria Grazia Gasparo +2
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale…