srph25

cmu-vision.github.io

diff_unrolled_admm_onenet

Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet

elasticluster

Create clusters of VMs on the cloud and configure them with Ansible

group-ksparse-temporal-cnns

Group k-Sparse Temporal Convolutional Neural Networks: Pre-training for Video Classification

group-sparse-change-point-detection

Python source code for reproducing the experiments described in the paper Towards reasoning based representations: Deep consistence seeking machine

group-sparse-outlier-detection

Python source code for reproducing the experiments described in the paper Robust Detection of Anomalies via Sparse Methods

keras

Deep Learning for humans

pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

tensorflow

Computation using data flow graphs for scalable machine learning

videoonenet

Source code 'VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing'

srph25/group-ksparse-temporal-cnns

Group k-Sparse Temporal Convolutional Neural Networks: Pre-training for Video Classification

srph25/cmu-vision.github.io

srph25/diff_unrolled_admm_onenet

Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet

srph25/elasticluster

Create clusters of VMs on the cloud and configure them with Ansible

srph25/group-sparse-change-point-detection

Python source code for reproducing the experiments described in the paper Towards reasoning based representations: Deep consistence seeking machine

srph25/group-sparse-outlier-detection

Python source code for reproducing the experiments described in the paper Robust Detection of Anomalies via Sparse Methods

srph25/keras

Deep Learning for humans

srph25/pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

srph25/tensorflow

Computation using data flow graphs for scalable machine learning

srph25/videoonenet

Source code 'VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing'