Pinned Repositories
ASCCA
Trace Lasso Regularization for Adaptive Sparse Canonical Correlation Analysis via Manifold Optimization Approach
awesome-Face_Recognition
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
BDPR
Blind Deconvolutional Phase Retrieval
Canonical-Correlation-Analysis
This project describes sparse canonical correlation analysis by python
deep_learning
Deep Learning Resources and Tutorials using Keras and Lasagne
DPRC_code
A Discriminative Projection and Representation-Based Classification Framework for Face Recognition
HyGrCls
This program is for hyper graph clustering for large hyper edges
KKDeng.github.io
mialm_code_share
A manifold inexact augmented Lagrangian method for nonsmooth optimization on Riemannian manifold
KKDeng's Repositories
KKDeng/Canonical-Correlation-Analysis
This project describes sparse canonical correlation analysis by python
KKDeng/DPRC_code
A Discriminative Projection and Representation-Based Classification Framework for Face Recognition
KKDeng/mialm_code_share
A manifold inexact augmented Lagrangian method for nonsmooth optimization on Riemannian manifold
KKDeng/ASCCA
Trace Lasso Regularization for Adaptive Sparse Canonical Correlation Analysis via Manifold Optimization Approach
KKDeng/KKDeng.github.io
KKDeng/a2dr
Anderson accelerated Douglas-Rachford splitting
KKDeng/DADAM
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
KKDeng/dengkk.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
KKDeng/erdos_neu
KKDeng/expRNN
Code to reproduce the NeurIPS 2019 paper "Trivializations for Gradient-Based Optimization on Manifolds" and the ICML 2019 paper "Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group"
KKDeng/geomstats
Computations and statistics on manifolds with geometric structures.
KKDeng/geoopt
Riemannian Adaptive Optimization Methods with pytorch optim
KKDeng/geotorch
Constrained optimization toolkit for PyTorch
KKDeng/GSPnP
Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"
KKDeng/KANGKANG-DENG.github.io
KKDeng/learning-to-learn-by-gradient-descent-by-gradient-descent
Pytorch version of NIPS'16 "Learning to learn by gradient descent by gradient descent"
KKDeng/Learning-to-optimize-on-SPD-manifolds
code of the CVPR 2020 paper "Learning to Optimize on SPD Manifolds"
KKDeng/NCVX
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
KKDeng/NIPS-rebuttal
KKDeng/Noisy-DPCP
Code of paper "Noisy Dual Principal Component Pursuit", ICML 2019
KKDeng/Open-L2O
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms
KKDeng/OpenMPL
An open multiple patterning framework
KKDeng/OPT
Implementation for <Orthogonal Over-Parameterized Training> in CVPR'21.
KKDeng/optimization-for-DS-lecture
KKDeng/OPTRA
optimal conjugate-free distributed primal-dual methods https://arxiv.org/pdf/1910.10666.pdf
KKDeng/prox-linear-experiments
The experiments investigate the performace of stochastic prox-linear based methods compared to the prototypical stochastic gradient methods.
KKDeng/RUN-CSP
KKDeng/ssnal_elastic
Efficient python implementation of SsNAL method to solve the elastic net problem
KKDeng/Test
KKDeng/TFPnP
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems (ICML 2020 Award Paper & JMLR 2022)