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Found 5 papers, 0 papers with code
Spike-EVPR: Deep Spiking Residual Network with Cross-Representation Aggregation for Event-Based Visual Place Recognition
This module is designed to extract features shared between the two representations and features specific to each.
FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events
To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR.
Predicting extreme events from data using deep machine learning: when and where
We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two.
Long-term prediction of chaotic systems with recurrent neural networks
Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic dynamical systems.
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius
Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, we uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized.
