Augmented intelligence for architectural design with conditional autoencoders: Semiramis case study

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Change detection (CD) is a critical task in remote sensing, especially for monitoring dynamic urban environments. In this work, we present a novel, self-supervised CD approach that leverages a two-player architecture to improve the reliability and accuracy ...

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain ac ...

Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm) to freeze the in-plane motion, which reduces the sensitivity of the dMRI s ...

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

Human motion prediction, the task of predicting future 3D human poses given a sequence of observed ones, has been mostly treated as a deterministic problem. However, human motion is a stochastic process: Given an observed sequence of poses, multiple future ...

Image super-resolution reconstructs a higher-resolution image from the observed low-resolution image. In recent years, machine learning models have been widely employed and deep learning networks have achieved state-of-the-art super-resolution performance. ...

In this paper, a learning-based image compression method that employs wavelet decomposition as a preprocessing step is presented. The proposed convolutional autoencoder is trained end-to-end to yield a target bitrate smaller than 0.15 bits per pixel across ...

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning me ...

Augmented intelligence for architectural design with conditional autoencoders: Semiramis case study

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Self-supervised Change Detection via Cooperative Learning: A Two-Player Model

The Recurrent Temporal Restricted Boltzmann Machine Captures Neural Assembly Dynamics in Whole-brain Activity

Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders

Product of experts for robot learning from demonstration

A Stochastic Conditioning Scheme for Diverse Human Motion Prediction

Towards Real-World Super-Resolution using Deep Neural Networks

Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition

Learning Search Strategies from Human Demonstrations

Variational Learning for Recurrent Spiking Networks