1.3k citations · 4.9k across the 23 of their papers we have counts for
24 papers
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte +1
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively rece…
Few-Shot Learning-Based Human Activity Recognition
Siwei Feng, Marco F. Duarte
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-s…
Design of Spectrally Shaped Binary Sequences via Randomized Convex Relaxation
Dian Mo, Marco F. Duarte
Wideband communication receivers often deal with the problems of detecting weak signals from distant sources received together with strong nearby interferers. When the techniques o…
Binary Sequence Set Design for Interferer Rejection in Multi-Branch Modulation
Dian Mo, Marco F. Duarte
Wideband communication is often expected to deal with a very wide spectrum, which in many environments of interest includes strong interferers. Thus receivers for the wideband comm…
Autoencoder Based Sample Selection for Self-Taught Learning
Siwei Feng, Han Yu, Marco F. Duarte
Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer lea…
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Siwei Feng, Marco F. Duarte
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant featur…