Csfrr-98

Csfrr-98's Stars

• ChaitanyaGogineni/BNN

  Designed to implement Bayesian modelling for Deep neural networks to classify the images with uncertainty.

  Language:Jupyter Notebook21

• laurent-vouriot/Bayesian-Neural-Networks-and-uncertainty

  Using bayesian neural networks to measure uncertainty in predictions

  Language:Jupyter Notebook2

• Ellie190/BCNN-for-Ocular-Disease-Classification

  A Bayesian Convolutional Neural Network model for classifying Cataract in Ocular Disease with measurements of uncertainty

  Language:Jupyter Notebook72

• Ste29/uncertainty-analysis

  Bayesian Neural Networks applied on mnist and emnist

  Language:Jupyter Notebook2

• mattoaellis/binary_stochastic_synapses

  Language:Python1

• SHITIANYU-hue/Efficient-motion-planning

  To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

  Language:MATLAB6912

• Daimacode/CRGEN

  Language:Python11

• Harry24k/bayesian-neural-network-pytorch

  PyTorch implementation of bayesian neural network [torchbnn]

  Language:Python49775

• RJ-T/NIPS2022_EP_BNP

  Official Implementation of NIPS 2022 paper Pre-activation Distributions Expose Backdoor Neurons

  Language:Python143

• ZK-Zhou/spikformer

  ICLR 2023, Spikformer: When Spiking Neural Network Meets Transformer

  Language:Python28945

• Thvnvtos/Nm-SNN

  Language:Python41

• tonggege001/MyNeuralCleanse

  复现了下Neural Cleanse这篇论文，真的是简单而有效，发在了okaland

  Language:Python296

• Trusted-AI/adversarial-robustness-toolbox

  Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

  Language:Python4.9k1.2k

• kangliucn/Fine-pruning-defense

  Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)

  Language:Python4617

• Tsinghua-LEMON-Lab/Dendritic-computing

  Language:Python144

• arppy/Neural-Cleanse-Pytorch

  competition

  Language:Python3

• bolunwang/backdoor

  Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

  Language:Python26963

• Eric-mingjie/network-slimming

  Network Slimming (Pytorch) (ICCV 2017)

  Language:Python909213

• Intelligent-Computing-Lab-Yale/PrivateSNN

  [AAAI 2022] PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks. https://arxiv.org/abs/2104.03414

  Language:Python71

• thomasaimondy/BRP-SNN

  The code for tuning Spiking Neural Network based on Biologically-plausible Reward Propagation

  Language:Python10

• ykubo82/bioCHL

  Neurons learn by predicting future activity

  Language:Python271

• rasmusbergpalm/DeepLearnToolbox

  Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

  Language:Matlab3.8k2.3k

• Yanqi-Chen/Gradient-Rewiring

  To appear in the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).

  Language:Python273

• QQQYang/snn

  construct a spiking neural network

  Language:Python233

• XDUSPONGE/SNN_benchmark

  25255

• fangwei123456/spikingjelly

  SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

  Language:Python1.4k246

• sx4n18/BrianSNN

  Brian simulator to do unsupervised training

  Language:Python1

• Shikhargupta/Spiking-Neural-Network

  Pure python implementation of SNN

  Language:Python1.1k286

• zxzhijia/Brian2STDPMNIST

  Brian 2 version of Paper "Unsupervised Learning of digit recognition using STDP"

  Language:Python13072

• djsaunde/lm-snn

  Using spiking neurons and spike-timing-dependent plasticity to classify the MNIST handwritten digits.

  Language:Python15646