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Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e.g., Gaussian distribution). Their advantages over GAN are that they can…

Computer Vision and Pattern Recognition · Computer Science 2020-09-24 Cong Geng , Jia Wang , Li Chen , Zhiyong Gao

An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech data. Recently, deep probabilistic generative models such as…

Computation and Language · Computer Science 2017-09-25 Wei-Ning Hsu , Yu Zhang , James Glass

Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when applied to sequences of human skeletons, standard variational autoencoders (VAEs) often…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Arafat Rahman , Shashwat Kumar , Laura E. Barnes , Anuj Srivastava

Existing neural architecture representation learning methods focus on continuous representation learning, typically using Variational Autoencoders (VAEs) to map discrete architectures onto a continuous Gaussian distribution. However,…

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec,…

Computer Vision and Pattern Recognition · Computer Science 2018-05-31 Neal Jean , Sherrie Wang , Anshul Samar , George Azzari , David Lobell , Stefano Ermon

Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and…

Machine Learning · Computer Science 2020-08-28 Henry Li , Ofir Lindenbaum , Xiuyuan Cheng , Alexander Cloninger

Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve…

Machine Learning · Computer Science 2023-11-14 Borui Cai , Shuiqiao Yang , Longxiang Gao , Yong Xiang

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function…

Machine Learning · Statistics 2021-01-05 Carl Doersch

Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to efficiently solve the generation problem for high dimensional…

Machine Learning · Computer Science 2021-03-02 A. Asperti , D. Evangelista , E. Loli Piccolomini

Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way. There is still however an open area of investigation into guiding a neural network to…

Machine Learning · Computer Science 2023-05-17 Vaishnavi Patil , Matthew Evanusa , Joseph JaJa

Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In…

Machine Learning · Computer Science 2025-01-29 Mayukh Deb , Mainak Deb , N. Apurva Ratan Murty

The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot…

Machine Learning · Statistics 2019-11-27 Emile Mathieu , Charline Le Lan , Chris J. Maddison , Ryota Tomioka , Yee Whye Teh

We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is…

Machine Learning · Computer Science 2019-02-25 Xiaopeng Li , Zhourong Chen , Leonard K. M. Poon , Nevin L. Zhang

We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide applicability of Graph Convolutional Neural Networks…

Machine Learning · Computer Science 2021-12-03 Xiang Gao , Wei Hu , Guo-Jun Qi

Deep generative models are increasingly becoming integral parts of the in silico molecule design pipeline and have dual goals of learning the chemical and structural features that render candidate molecules viable while also being flexible…

Biomolecules · Quantitative Biology 2021-06-08 Yair Schiff , Vijil Chenthamarakshan , Karthikeyan Natesan Ramamurthy , Payel Das

VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we…

Machine Learning · Computer Science 2017-07-18 Gaëtan Hadjeres , Frank Nielsen , François Pachet

Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through…

Machine Learning · Computer Science 2022-05-04 Alon Berliner , Guy Rotman , Yossi Adi , Roi Reichart , Tamir Hazan

The manifold hypothesis states that high-dimensional data can be modeled as lying on or near a low-dimensional, nonlinear manifold. Variational Autoencoders (VAEs) approximate this manifold by learning mappings from low-dimensional latent…

Machine Learning · Statistics 2021-03-03 Marissa C. Connor , Gregory H. Canal , Christopher J. Rozell

The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the ever growing set of unlabelled images, semi-supervised learning…

Machine Learning · Statistics 2020-11-18 Mizu Nishikawa-Toomey , Lewis Smith , Yarin Gal

Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data. When the correlation structure among data points is available, previous work proposed…

Machine Learning · Computer Science 2019-12-20 Da Tang , Dawen Liang , Nicholas Ruozzi , Tony Jebara