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Name: Harshith MohanKumar
Type: User
Company: @Intel
Bio: MSCS @ UC Riverside Trying to figure out how to make Jarvis so he can solve my life problems.
Location: Riverside, CA
Name: Harshith MohanKumar
Type: User
Company: @Intel
Bio: MSCS @ UC Riverside Trying to figure out how to make Jarvis so he can solve my life problems.
Location: Riverside, CA
100 Day ML Challenge to learn and develop machine learning products. Since this is my second time performing this challenge, this time around I will be focusing more on the production enviroment rather than the concepts and theory behind ML/DL models. I will be placing heavy emphasis on the ML pipeline and the process of taking an ML model and applying into a real-world application.
100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics to more advanced state of the art models.
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
This repository contains a small python address book project.
An awesome README template to jumpstart your projects!
Tool to capture Boson USB video in Linux
[WACV 2024] Code for "CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning"
Open source simulator for autonomous robotics built on Unreal Engine with support for Unity
Code repo for "DAVANet: Stereo Deblurring with View Aggregation" (CVPR'19, Oral)
An efficient video loader for deep learning with smart shuffling that's super easy to digest
TensorFlow documentation
Pytorch implementation of FTNet for Semantic Segmentation on SODA, SCUT Seg, and MFN Datasets
Graphs are widely in use to model related instances of data attributed with properties providing rich spatial information. While a lot of classical graph-related problems have been solved with the advent of Graph Neural Networks (GNN), Spatio-Temporal data poses a new challenge. We propose GraphCoReg: a novel methodology to perform regression on spatio-temporal data, in a Semi-Supervised Learning (SSL) setting using co-training. Our co-training approach exploits two views of the dataset using two temporal Graph Neural Networks (GNNs) - an Attention-based GNN (A3TGCN) and a Long Short Term Memory GNN (GCLSTM). Additionally, methodologies to incrementally add the pseudo-targets to training data have been described. We finally compare the performance of the semi-supervised model with equivalent supervised models. This approach has been tested on the MetrLA dataset for traffic forecasting.
This proposal proposes a multi-modal multi-phase pipeline to tackle television show segmentation on the Rosenthal videotape collection. The two-stage pipeline will begin with feature filtering using pre-trained classifiers and heuristic-based approaches. This stage will produce noisy title sequence segmented data containing audio, video, and possibly text. These extracted multimedia snippets will then be passed to the second pipeline stage. In the second stage, the extracted features from the multimedia snippets will be clustered using RNN-DBSCAN. Title sequence detection is possibly the most efficient path to high precision segmentation for the first and second tiers of the Rosenthal collection (which have fairly structured recordings). This detection algorithm may not bode well for the more unstructured V8+ and V4 VCR tapes in the Rosenthal collection. Therefore the goal is to produce accurate video cuts and split metadata results for the first and second tiers of the Rosenthal collection.
Google Summer of Code 2020 with TensorFlow: Final Work Product
CNN-based audio segmentation toolkit. Allows to detect speech, music and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.
This repo will house all our course material and code snippets from the Introduction to Machine Learning Class
Deep Learning for humans
This program will use image recognition software to determine if the image provided contains a modak or not.
Program created to play the game reversi.
Python program that allows users to play reversi with a basic ai.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.