Abhishek Kumar's repositories
Bayesian-Neural-Networks
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
bnn
Bayesian Neural Network in PyTorch
C3
PyTorch Implementation of C3
chatgpt-prompts-for-academic-writing
This list of writing prompts covers a range of topics and tasks, including brainstorming research ideas, improving language and style, conducting literature reviews, and developing research plans.
DCV
Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
DEC-keras
Keras implementation for Deep Embedding Clustering (DEC)
DEC-Pytorch
Deep Embedding for Clustering (accuracy can reach over 87.5%)
deep-compression
Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626
DIVA
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
dpc-matlab
dpc算法实现
emcee
The Python ensemble sampling toolkit for affine-invariant MCMC
Microsoft-Activation-Scripts
A Windows and Office activator using HWID / Ohook / KMS38 / Online KMS activation methods, with a focus on open-source code and fewer antivirus detections.
ProPos
Self-Supervised Learning for Deep Clustering (TPAMI 2022)
ptemcee
A parallel-tempered version of emcee.
scikit-learn
scikit-learn: machine learning in Python
self_supervised
Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks.
SpaceMAP
SpaceMAP is a dimensionality reduction method utilizing the local and global intrinsic dimensions of the data to better alleviate the 'crowding problem' analytically.
Ultra-DPC
Ultra-DPC:Ultra-scalable and Index-free Density Peak Clustering
umato
Uniform Manifold Approximation with Two-phase Optimization (IEEE VIS 2022 short)
Unsupervised-Classification
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
UP-DPC
UP-DPC: Ultra-scalable Parallel Density Peak Clustering
vissl
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.