Controlling the spectral norm of implicitly linear layers (e.g., convolutional layers)
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Updated
May 17, 2024 - Python
Controlling the spectral norm of implicitly linear layers (e.g., convolutional layers)
Wavelet Scattering Transform applied for Geo Sciences.
This code trains a CNN in Keras to classify cell images (infected/uninfected). It sets up data generators, defines model architecture with convolutional layers, applies regularization, configures callbacks, and trains the model for binary classification.
Implementation of SoundtStream from the paper: "SoundStream: An End-to-End Neural Audio Codec"
GPU-accelerated Neural Network layers using Approximate Multiplications for PyTorch
Implementation of the NFNets from the paper: "ConvNets Match Vision Transformers at Scale" by Google Research
Code for Spectral Norm of Convolutional Layers with Circular and Zero Paddings and Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
A 1D implementation of a deformable convolutional layer in PyTorch with a few tricks.
A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. This repository contains the code for CNN with a categorical classification dataset.
In this project we have explored the use of imaging time series to enhance forecasting results with Neural Networks. The approach has revealed itself to be extremely promising as, both in combination with an LSTM architecture and without, it has out-performed the pure LSTM architecture by a solid margin within our test datasets.
2D Convolution from NumPy
Determine feasible grasp positions and orientations using a spherically transformed dataset.
Memory efficient convolution networks
MegBox is an easy-to-use, well-rounded and safe toolbox of MegEngine. Aim to imporving usage experience and speeding up develop process.
End-of-studies project about SNN and motion recognition
A Python implementation of the InverSynth method (Barkan, Tsiris, Koenigstein, Katz)
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
Multi-scale version of ROCKET: a random convolutional kernel machine learning method
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