Conditional Gradients For The Approximately Vanishing Ideal

Related tags

Deep Learningcgavi
Overview

Conditional Gradients For The Approximately Vanishing Ideal

Code for the paper:

Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. To Appear in Proceedings of AISTATS.

Installation guide

Download the repository and store it in your preferred location, say ~/tmp.

Open your terminal and navigate to ~/tmp.

Run the command:

$ conda env create --file environment.yml

This will create the conda environment approximately_vanishing_ideal.

Activate the conda environment with:

$ conda activate approximately_vanishing_ideal

In the file global_.py, change the value of gpu_memory_ to the maximum amount of gpu you wish to use to perform computations.

Run the tests:

>>> python3 -m unittest

No errors should occur.

Execute the experiments:

>>> python3 experiments.py

This will create a folder named data_frames, which continues subfolders containing the experiment results.

The experiments can be displayed as latex_code by executing:

>>> experiments_results.py
You might also like...
Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

Argument Extraction by Generation Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21' Dependencies pytorch=1.6 tr

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms
A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms

Denoising Diffusion Probabilistic Model for Proteins Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to gen

This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

 CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution This is the official implementation code of the paper "CondLaneNe

Cancer metastasis detection with neural conditional random field (NCRF)
Cancer metastasis detection with neural conditional random field (NCRF)

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

Synthesizing and manipulating 2048x1024 images with conditional GANs
Synthesizing and manipulating 2048x1024 images with conditional GANs

pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio

 Official implementation of the ICCV 2021 paper
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".

The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings and that the spatial embeddings make minor contributions, increasing the need for high-quality content embeddings and thus increasing the training difficulty.

Releases(v2.0.0)
Owner
IOL Lab @ ZIB
IOL Lab @ ZIB
(to be released) [NeurIPS'21] Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs

Higher-Order Transformers Kim J, Oh S, Hong S, Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs, NeurIPS 2021. [arxiv] W

Jinwoo Kim 44 Dec 28, 2022
Twin-deep neural network for semi-supervised learning of materials properties

Deep Semi-Supervised Teacher-Student Material Synthesizability Prediction Citation: Semi-supervised teacher-student deep neural network for materials

MLEG 3 Dec 14, 2022
(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

141 Dec 29, 2022
The official implementation code of "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction."

PlantStereo This is the official implementation code for the paper "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction".

Wang Qingyu 14 Nov 28, 2022
(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

BRNet Introduction This is a release of the code of our paper Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds,

86 Oct 05, 2022
SIR model parameter estimation using a novel algorithm for differentiated uniformization.

TenSIR Parameter estimation on epidemic data under the SIR model using a novel algorithm for differentiated uniformization of Markov transition rate m

The Spang Lab 4 Nov 30, 2022
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
Zalo AI challenge 2021 task hum to song

Zalo AI challenge 2021 task Hum to Song pipeline: Chuẩn bị dữ liệu cho quá trình train: Sửa các file đường dẫn trong config/preprocess.yaml raw_path:

Vo Van Phuc 105 Dec 16, 2022
Gesture-controlled Video Game. Just swing your finger and play the game without touching your PC

Gesture Controlled Video Game Detailed Blog : https://www.analyticsvidhya.com/blog/2021/06/gesture-controlled-video-game/ Introduction This project is

Devbrat Anuragi 35 Jan 06, 2023
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection

Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection This material is supplementray code for paper accepted in ICDAR 2021 We h

NCSOFT 30 Dec 21, 2022
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring

Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas

YunzhuangS 2 May 02, 2022
A simple version for graphfpn

GraphFPN: Graph Feature Pyramid Network for Object Detection Download graph-FPN-main.zip For training , run: python train.py For test with Graph_fpn

WorldGame 67 Dec 25, 2022
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.

st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha

Diplodocus 258 Jan 02, 2023
Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Intro to Deep Learning Materials are part of North South University (NSU) Intro to Deep Learning with PyTorch workshop series. (Slides) Related materi

Hasib Zunair 9 Jun 08, 2022
Convert Table data to approximate values with GUI

Table_Editor Convert Table data to approximate values with GUIs... usage - Import methods for extension Tables. Imported method supposed to have only

CLJ 1 Jan 10, 2022
JDet is Object Detection Framework based on Jittor.

JDet is Object Detection Framework based on Jittor.

135 Dec 14, 2022
Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization"

Riggable 3D Face Reconstruction via In-Network Optimization Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimizati

130 Jan 02, 2023
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022