TextUnderstandingTsetlinMachineUsing the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications
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mllpThe code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
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WhiteBox-Part1In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
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bsplBit-Shift-Print Loop
Stars: ✭ 17 (-70.69%)
3D-GuidedGradCAM-for-Medical-ImagingThis Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
Stars: ✭ 60 (+3.45%)
hierarchical-dnn-interpretationsUsing / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Stars: ✭ 110 (+89.66%)
catacombThe simplest machine learning library for launching UIs, running evaluations, and comparing model performance.
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compvInsanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)
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concept-based-xaiLibrary implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
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SkillNERA (smart) rule based NLP module to extract job skills from text
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expmrcExpMRC: Explainability Evaluation for Machine Reading Comprehension
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KerasMNISTKeras MNIST for Handwriting Detection
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VAE-Latent-Space-ExplorerInteractive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.
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cDCGANPyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)
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digitRecognitionImplementation of a digit recognition using my Neural Network with the MNIST data set.
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XAIatERUM2020Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
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graphkit-learnA python package for graph kernels, graph edit distances, and graph pre-image problem.
Stars: ✭ 87 (+50%)
dclareForMPSAdding declarative, reactive and incremental rules to MPS
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computer-vision-notebooks👁️ An authorial set of fundamental Python recipes on Computer Vision and Digital Image Processing.
Stars: ✭ 89 (+53.45%)
dlime experimentsIn this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (-63.79%)
ATGValidatoriOS validation framework with form validation support
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DCGAN-PytorchA Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"
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Pytorch-PCGradPytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
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bert attn vizVisualize BERT's self-attention layers on text classification tasks
Stars: ✭ 41 (-29.31%)
greek scansionPython library for automatic analysis of Ancient Greek hexameter. The algorithm uses linguistic rules and finite-state technology.
Stars: ✭ 16 (-72.41%)
Bounding-Box-Regression-GUIThis program shows how Bounding-Box-Regression works in a visual form. Intersection over Union ( IOU ), Non Maximum Suppression ( NMS ), Object detection, 边框回归,边框回归可视化,交并比,非极大值抑制,目标检测。
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digit recognizerCNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).
Stars: ✭ 27 (-53.45%)
responsible-ai-toolboxThis project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
Stars: ✭ 615 (+960.34%)
pyCeterisParibusPython library for Ceteris Paribus Plots (What-if plots)
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graspEssential NLP & ML, short & fast pure Python code
Stars: ✭ 58 (+0%)
Transformer-MM-Explainability[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Stars: ✭ 484 (+734.48%)
neuro-symbolic-sudoku-solver⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
Stars: ✭ 60 (+3.45%)
transformers-interpretModel explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Stars: ✭ 861 (+1384.48%)
Hand-Digits-RecognitionRecognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.
Stars: ✭ 11 (-81.03%)
Fun-with-MNISTPlaying with MNIST. Machine Learning. Generative Models.
Stars: ✭ 23 (-60.34%)
megMolecular Explanation Generator
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tensorflow-mnist-AAETensorflow implementation of adversarial auto-encoder for MNIST
Stars: ✭ 86 (+48.28%)
datafsmMachine Learning Finite State Machine Models from Data with Genetic Algorithms
Stars: ✭ 14 (-75.86%)
Awesome-Human-Activity-RecognitionAn up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.
Stars: ✭ 72 (+24.14%)
mindsdb serverMindsDB server allows you to consume and expose MindsDB workflows, through http.
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CNN-MNISTCNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)
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HigraHierarchical Graph Analysis
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cluttered-mnistExperiments on cluttered mnist dataset with Tensorflow.
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MNIST-CoreMLPredict handwritten digits with CoreML
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gans-2.0Generative Adversarial Networks in TensorFlow 2.0
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LeNet-from-ScratchImplementation of LeNet5 without any auto-differentiate tools or deep learning frameworks. Accuracy of 98.6% is achieved on MNIST dataset.
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BP-NetworkMulti-Classification on dataset of MNIST
Stars: ✭ 72 (+24.14%)
PyTsetlinMachineCUDAMassively Parallel and Asynchronous Architecture for Logic-based AI
Stars: ✭ 37 (-36.21%)