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Found 15 papers, 13 papers with code
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task.
Precipitation nowcasting with generative diffusion models
This approach, in comparison to recent deep learning models, substantially outperformed them in terms of overall performance.
Head Rotation in Denoising Diffusion Models
Denoising Diffusion Models (DDM) are emerging as the cutting-edge technology in the realm of deep generative modeling, challenging the dominance of Generative Adversarial Networks.
Image Embedding for Denoising Generative Models
Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation.
Comparing the latent space of generative models
Different encodings of datapoints in the latent space of latent-vector generative models may result in more or less effective and disentangled characterizations of the different explanatory factors of variation behind the data.
MicroRacer: a didactic environment for Deep Reinforcement Learning
MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning.
Enhancing variational generation through self-decomposition
In this article we introduce the notion of Split Variational Autoencoder (SVAE), whose output $\hat{x}$ is obtained as a weighted sum $\sigma \odot \hat{x_1} + (1-\sigma) \odot \hat{x_2}$ of two generated images $\hat{x_1},\hat{x_2}$, and $\sigma$ is a {\em learned} compositional map.
Dissecting FLOPs along input dimensions for GreenAI cost estimations
The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods.
A survey on Variational Autoencoders from a GreenAI perspective
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 data.
Syllabification of the Divine Comedy
We jointly provide an online vocabulary containing, for each word, information about its syllabification, the location of the tonic accent, and the aforementioned synalephe propensity, on the left and right sides.
Variance Loss in Variational Autoencoders
The minor variance creates a mismatch between the actual distribution of latent variables and those generated by the second VAE, that hinders the beneficial effects of the second stage.
Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders
In the loss function of Variational Autoencoders there is a well known tension between two components: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space.
Sparsity in Variational Autoencoders
Working in high-dimensional latent spaces, the internal encoding of data in Variational Autoencoders becomes naturally sparse.
Crawling in Rogue's dungeons with (partitioned) A3C
Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender.
The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images
The lack, due to privacy concerns, of large public databases of medical pathologies is a well-known and major problem, substantially hindering the application of deep learning techniques in this field.
