aayush2710
EE Sophomore @ IIT Hyderabad CP | Machine Learning | Deep Learning | Computer Vision | ML in Communications
Hyderabad / New Delhi
aayush2710's Stars
junegunn/vim-plug
:hibiscus: Minimalist Vim Plugin Manager
tpope/vim-surround
surround.vim: Delete/change/add parentheses/quotes/XML-tags/much more with ease
vim-syntastic/syntastic
Syntax checking hacks for vim
airblade/vim-gitgutter
A Vim plugin which shows git diff markers in the sign column and stages/previews/undoes hunks and partial hunks.
cleverhans-lab/cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
tpope/vim-commentary
commentary.vim: comment stuff out
editorconfig/editorconfig-vim
EditorConfig plugin for Vim
mileszs/ack.vim
Vim plugin for the Perl module / CLI script 'ack'
bethgelab/foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
jayinai/data-science-question-answer
A repo for data science related questions and answers
lextm/windowsterminal-shell
Install/uninstall scripts for Windows Terminal context menu items
datalogue/keras-attention
Visualizing RNNs using the attention mechanism
auspicious3000/SpeechSplit
Unsupervised Speech Decomposition Via Triple Information Bottleneck
scopeInfinity/Video2Description
Video to Text: Natural language description generator for some given video. [Video Captioning]
StevenMHernandez/ESP32-CSI-Tool
Extract Channel State Information from WiFi-enabled ESP32 Microcontroller. Active and Passive modes available. (https://stevenmhernandez.github.io/ESP32-CSI-Tool/)
radioML/dataset
Open RadioML Synthetic Benchmark Dataset
1Konny/VIB-pytorch
Pytorch implementation of Deep Variational Information Bottleneck
Retsediv/WIFI_CSI_based_HAR
Human Activity Recognition based on WiFi Channel State Information
snap-stanford/GIB
Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs
jssprz/video_captioning_datasets
Summary about Video-to-Text datasets. This repository is part of the review paper *Bridging Vision and Language from the Video-to-Text Perspective: A Comprehensive Review*
alexalemi/vib_demo
qieaaa/Deep-Architectures-for-Modulation-Recognition
deep learning implement for modulation classification ,codes for paper <Deep Architectures for Modulation Recognition>
zhuchen03/VIBNet
Compressing Neural Networks using the Variational Information Bottleneck
SeojinBang/VIBI
Explaining a black-box using Deep Variational Information Bottleneck Approach
rajan9519/Background-subtraction
ravidziv/Information-bottleneck
Python implementation of the infomration bottleneck method (tishby et al, 1999)
jssprz/visual_syntactic_embedding_video_captioning
Source code of the paper titled *Improving Video Captioning with Temporal Composition of a Visual-Syntactic Embedding*
kairess/semantic-segmentation-pytorch
Semantic Segmentation and Foreground and Background Separation
peppermenta/iith-dashboard-pwa
New and Improved PWA with additional features
Surya291/GRE_PREP
This is a guide for how one can prepare for GRE within a month's duration.