vsingh-group Goto Github PK

coreset

crow

datasetpooling

dcnn

This is the published codes for Dilated Convolutional Neural Networks (DCNN) for Sequential Manifold-valued Data in Neuroimaging ICCV 2019

deepcg

deephermites

download-the-world

:earth_americas: Downloading the world using Google Map's 3D models and nightmare.

dual-glow

[ICCV'19] DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer

dual_manifold_glow

This is the official webpage of the Flow-based Generative Models for Learning Manifold to Manifold Mappings in AAAI 2021

dynamic-k-activation

Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks

f1-opt

fairalm

filterflow

Filter Flow made Practical: Massively Parallel and Lock-Free

framequant

glapgan

ic-loss

PyTorch and Tensorflow implementation of inverse contrastive loss - https://arxiv.org/abs/2102.08343

icl

Learning invariant representations using Inverse Contrastive Loss

icml2017

This folder contains two hypothesis tests covered in my ICML work for when pooling multi-source datasets can help.

im-unpack

invariant-net

Learning View Invariant Semantic Segmentation for UAV Video Sequences

isbi2020

kgmm_interpolation

Interpolation on the manifold of k component GMMs

kpf-flow

lcodec-deep-unlearning

Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"

lvgm-cvpr2016

Latent Variable Graphical Model Selection using Harmonic Analysis: Applications to the Human Connectome Project (HCP) Won Hwa Kim∗ Hyunwoo J. Kim∗ Nagesh Adluru Vikas Singh University of Wisconsin-Madiso

mcrepar

Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks

menets

Mixed Effects Neural Networks (MeNets) with Applications to Gaze Estimation

mmglm_htcondor

MATLAB Scripts to perform MMGLMs over HTCondor

mmglmaws

Code to run multiple jobs parallely over AWS (similar to HTCondor)

mobiledet-pytorch

PyTorch Implementation of MobileDet (https://arxiv.org/abs/2004.14525v3) backbones.