most citedBayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information

57 citations · 88 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP20232 cited

Robust Interference Mitigation techniques for Direct Position Estimation

Haoqing Li, Shuo Tang, Peng Wu +1

Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventiona…

cs.RO202357 cited

Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information

Francisco Pastor, Jorge García-González, Juan M. Gandarias +4

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encount…

cs.LG20231 cited

Jammer classification with Federated Learning

Peng Wu, Helena Calatrava, Tales Imbiriba +1

Jamming signals can jeopardize the operation of GNSS receivers until denying its operation. Given their ubiquity, jamming mitigation and localization techniques are of crucial impo…

eess.IV202328 cited

Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks

Ricardo Augusto Borsoi, Tales Imbiriba, Pau Closas

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers)…

math.ST2023

On Parametric Misspecified Bayesian Cramér-Rao bound: An application to linear Gaussian systems

Shuo Tang, Gerald LaMountain, Tales Imbiriba +1

A lower bound is an important tool for predicting the performance that an estimator can achieve under a particular statistical model. Bayesian bounds are a kind of such bounds whic…