Laplacian Score-regularized Concrete Autoencoders

Last update: Dec 07, 2022

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Deep Learning lscae

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

torch >= 1.9
scikit-learn >= 0.24
omegaconf >= 2.0.6
scipy >= 1.6.0
matplotlib

How to use:

Install the package from pypi: pip install lscae

import lscae
import torch
from omegaconf import OmegaConf

# define you cfg parameters
cfg = OmegaConf.create({
    "input_dim": 100 })
# define you dataset (Torch based)
dataset = torch.utils.data.Dataset(...)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
lscae.Lscae(kwargs=cfg).select_features(dataloader)

Please see the full example here

Owner

JS

Applied Data Scientist

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

ROS Basics and TurtleSim

The personal repository of the work: *DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer*.

9th place solution

The code for paper "Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation" which is accepted by AAAI 2022

Count GitHub Stars ⭐

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"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

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Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

Fully Convolutional DenseNet (A.K.A 100 layer tiramisu) for semantic segmentation of images implemented in TensorFlow.

Implementation of the pix2pix model on satellite images

MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily

Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

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