A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.
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Updated
Oct 10, 2025 - Python
A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.
An Open-Source Library for Training Binarized Neural Networks
Highly optimized inference engine for Binarized Neural Networks
Tools and libraries to run neural networks in Minecraft ⛏️
Reference implementations of popular Binarized Neural Networks
Implementation for the paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization"
Some recent Quantizing techniques on PyTorch
[ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binarization.
Contains code for Binary, Ternary, N-bit Quantized and Hybrid CNNs for low precision experiments.
Reproduction of "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization" for the Reproducibility challenge@NeurIPS19
Deep Learning Framework with a specialisation aimed for Binarized Neural Networks.
Code implementation of our AISTATS'21 paper "Mirror Descent View for Neural Network Quantization"
Binarized Neural Network DoA estimation
Neural Property Approximate Quantifier
The official repository for the paper LAB: Learnable Activation Binarizer for Binary Neural Networks.
My PhD thesis with all its source files, including all .tex files and images created, as well as the slides of my defense.
An implementation of the Binarized Neural Networks
BNN verification dataset for Max-SAT Evaluation 2020, MIPLIB 2024, and Pseudo-Boolean Competition 2025
Introduce a Software Acceleration filter for Real Time Object Recognition with Binary Neural Network on FPGA
Progressive Neural Architecture Search coupled with Binarized CNNs to search for resource efficient and accurate architectures.
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