Tech Xplore

Ferroelectric memory enables one chip to sample randomness and compute for generative AI

For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...
GitHub

RAVE: Realtime Audio Variational autoEncoder

Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (article link) by Antoine Caillon and Philippe Esling. If you use RAVE as a part of a music ...
Scientific Research Publishing

Variational Auto-Encoder and Speeded-Up Robust Features Hybrid Model for Anomaly Detection and Localization in Video Sequence with Scale Variation ()

Jomo Kenyatta University of Agriculture and Technology, Juja, Kiambu County, Kenya. Where KL denotes the Kullback-Leibler divergence, and p(z) is a prior distribution over the latent space (typically ...
GitHub

Variational Autoencoder in tensorflow and pytorch

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...
IEEE

Distribution-Aware and Class-Adaptive Aggregation for Few-Shot Hyperspectral Image Classification

Abstract: Recently, few-shot learning based on meta-learning has shown great potential in hyperspectral image classification (HSIC) due to its excellent adaptability to limited training samples.
IEEE

Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach

Abstract: Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms.
Nature

Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study

Medical machine learning frameworks have received much attention in recent years. The recent COVID-19 pandemic was also accompanied by a surge in proposed machine learning algorithms for tasks such as ...
Frontiers

Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification ...
PNAS

Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT

Single-cell multimodal assays provide an unprecedented opportunity for investigating heterogeneity of cell types and novel associations with disease and development. Although analyses of such ...