Category: Machine Learning

Articles About Machine Learning

Machine Learning

A Legate library that aims to provide a distributed and accelerated drop-in replacement

Legate NumPy Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the final example of the Python CFD course completely unmodified on 2048 A100 GPUs in a DGX SuperPOD and achieve good weak scaling. Legate NumPy works best for programs that have very large arrays of data that cannot fit in the memory of a […]

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Machine Learning

Implementation of different architectures for emotion recognition in conversations

Emotion Recognition in Conversations Unlike other emotion detection models, these techniques consider the party-states and inter-party dependencies for modeling conversational context relevant to emotion recognition. The primary purpose of all these techniques are to pretrain an emotion detection model for empathetic dialogue generation. Interaction among different controlling variables during a dyadic conversation between persons X and Y. Grey and white circles represent hidden and observed variables, respectively. P represents personality, U represents utterance, S represents interlocutor state, I represents interlocutor […]

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Machine Learning

2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics

Fluid Simulation 2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions. Usage Download this repo and store it in your computer. Open a terminal and go to the root directory of this folder. Make sure you have installed the needed dependencies by typing: $ pip install numpy $ pip install matplotlib $ pip install ffmpeg Note: Go to Install FFmpeg on Windows section if you haven’t installed FFmpeg software locally before. It must […]

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Machine Learning

Multiple types of NN model optimization environments

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. And, Intel iHD GPU (iGPU) support. 1. Environment Docker 20.10.5, build 55c4c88 2. Model optimization environment to be built Ubuntu 20.04 x86_64 CUDA 11.0 cuDNN 8.0 TensorFlow v2.4.1 (MediaPipe Custom OP, FlexDelegate, XNNPACK enabled) tflite_runtime v2.4.1 (MediaPipe Custom OP, FlexDelegate, XNNPACK enabled) edgetpu-compiler flatc 1.12.0 TensorRT cuda11.0-trt7.1.3.4-ga-20200617 PyTorch 1.8.1+cu110 TorchVision 0.9.1+cu110 TorchAudio 0.8.1 OpenVINO 2021.3.394 […]

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Machine Learning

Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper “Hypercorrelation Squeeze for Few-Shot Segmentation” by Juhong Min, Dahyun Kang, and Minsu Cho. Implemented on Python 3.7 and Pytorch 1.5.1. Requirements Python 3.7 PyTorch 1.5.1 cuda 10.1 tensorboard 1.14 Conda environment settings: conda create -n hsnet python=3.7 conda activate hsnet conda install pytorch=1.5.1 torchvision cudatoolkit=10.1 -c pytorch conda install -c conda-forge tensorflow pip install tensorboardX Preparing Few-Shot Segmentation Datasets Download following datasets: 1. PASCAL-5i Download PASCAL VOC2012 devkit […]

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Machine Learning

Make Telegram group call with MTProto Api using Pyrogram and WebRTC

PyTgCalls This project allow to make Telegram group call with MTProto Api using Pyrogram and WebRTC, this is possible thanks to the power of NodeJS’s WebRTC library, socketio-client and @evgeny-nadymov Problem with pyrogram? If do you have problems with pyrogram, reinstall by this command pip install git+https://github.com/pyrogram/pyrogram -U Live stream or ffmpeg live conversion stopped? Check before if is changing the size of file(Is a method to check if ffmpeg is alive). If is alive and stream is stopped, report […]

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Machine Learning, NLP

Score-Based Generative Modeling through Stochastic Differential Equations

score_sde_pytorch PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral). This repo contains a PyTorch implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole What does this code do? Aside from the NCSN++ and DDPM++ models in our paper, this codebase also re-implements many previous score-based models in one place, including NCSN from Generative Modeling by Estimating Gradients of […]

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Machine Learning, NLP

Code for Multiple Instance Active Learning for Object Detection

MI-AOD This is the code for Multiple Instance Active Learning for Object Detection, CVPR 2021. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images […]

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Machine Learning, NLP

RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

RfD-Net Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias NießnerIn CVPR, 2021. From an incomplete point cloud of a 3D scene (left), our method learns to jointly understand the 3D objects and reconstruct instance meshes as the output (right). Install This implementation uses Python 3.6, Pytorch1.7.1, cudatoolkit 11.0. We recommend to use conda to deploy the environment. conda env create -f environment.yml conda activate rfdnet pip install -r requirements.txt Next, compile the external libraries by python setup.py build_ext –inplace Install PointNet++ […]

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