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

GitHub Repository

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

This implements one of result networks from Large-scale evolution of image classifiers

GPT, but made only out of gMLPs

Towhee is a flexible machine learning framework currently focused on computing deep learning embeddings over unstructured data.

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Combining Diverse Feature Priors

Here I will explain the flow to deploy your custom deep learning models on Ultra96V2.

3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

OpenLT: An open-source project for long-tail classification

ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation

Toward Multimodal Image-to-Image Translation

Sign Language Translation with Transformers (COLING'2020, ECCV'20 SLRTP Workshop)

Multi Camera Calibration

Exploring Machine Learning Models for detecting anomalous behavior in credit-card transactions. It's crucial that credit-card companies are able to recognize fraudulent activity so that customers are not charged for items they didn't purchase.

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