Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
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Updated
Aug 4, 2020 - Python
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments
A template for doing regression from images with pytorch
[ECCV 2022] The official experimental code of "Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives"
CME Arrival Time Prediction Using Convolutional Neural Network
PyTorch Deep Learning Framework for Multimedia
Car price predictor using a convolutional neural network. Final assignment for the Neural Networks course.
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This is the project Behavioral Cloning completed under the Udacity Self Driving Car Engineer Nano-degree Program
Estimating graph vertex/edge counts from rendered images using a CNN, benchmarked against a GCN baseline and a density heuristic — with Grad-CAM, shortcut-learning probes, and failure-case analysis of what the model actually learns. Includes a deployed Streamlit demo app.
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