Search Results for author:
Found 3 papers, 1 papers with code
Improving Neural Predictivity in the Visual Cortex with Gated Recurrent Connections
Computational models of vision have traditionally been developed in a bottom-up fashion, by hierarchically composing a series of straightforward operations - i. e. convolution and pooling - with the aim of emulating simple and complex cells in the visual cortex, resulting in the introduction of deep convolutional neural networks (CNNs).
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings
With our method, we observed robust results in terms of ac-curacy for a four-labels classification problem, with limited available data.
Physics-Informed Machine Learning Simulator for Wildfire Propagation
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction.
BIG-bench Machine Learning
Physics-informed machine learning
