Search Results for author: Jorge Silva

Found 8 papers, 1 papers with code

AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

no code implementations NeurIPS 2020 Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva

The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection.

Decision Making reinforcement-learning +1

Multi-Task Learning with Incomplete Data for Healthcare

no code implementations6 Jul 2018 Xin J. Hunt, Saba Emrani, Ilknur Kaynar Kabul, Jorge Silva

Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalization performance.

Imputation Multi-Task Learning

RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering

no code implementations25 Apr 2018 Namita Lokare, Jorge Silva, Ilknur Kaynar Kabul

In this paper, we propose a robust feature engineering method, Randomized Union of Locally Linear Subspaces (RULLS).

Clustering Feature Engineering +1

Online Robust Principal Component Analysis with Change Point Detection

2 code implementations19 Feb 2017 Wei Xiao, Xiaolin Huang, Jorge Silva, Saba Emrani, Arin Chaudhuri

Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing.

Change Point Detection Two-sample testing

Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description

no code implementations17 Feb 2016 Deovrat Kakde, Arin Chaudhuri, Seunghyun Kong, Maria Jahja, Hansi Jiang, Jorge Silva

For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly.

General Classification Outlier Detection

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents

no code implementations NeurIPS 2010 Eric Wang, Dehong Liu, Jorge Silva, Lawrence Carin, David B. Dunson

An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.

Near-minimax recursive density estimation on the binary hypercube

no code implementations NeurIPS 2008 Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva

This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions.

Density Estimation

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