Search Results for author:
Found 13 papers, 8 papers with code
Position control of an acoustic cavitation bubble by reinforcement learning
The agent must choose the optimal pressure amplitude values to manipulate the bubble position in the range of $x/\lambda_0\in[0. 05, 0. 25]$.
Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model
A major challenge in medical image analysis is the automated detection of biomarkers from neuroimaging data.
Self-Supervised Pretraining for 2D Medical Image Segmentation
In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency.
Utility of Equivariant Message Passing in Cortical Mesh Segmentation
Our evaluation shows that GNNs outperform EGNNs on aligned meshes, due to their ability to leverage the presence of a global coordinate system.
Improving Self-Supervised Learning-based MOS Prediction Networks
Telecommunications (for voice and video), and speech synthesis systems (for generated speech) are a few of the many applications of the method.
Reconstructing nodal pressures in water distribution systems with graph neural networks
The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible.
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems
The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data.
Predicting the flow field in a U-bend with deep neural networks
This database was used to train an encoder-decoder style deep convolutional neural network to predict the velocity distribution from the geometry.
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment.
Self-Attention Networks for Intent Detection
Self-attention networks (SAN) have shown promising performance in various Natural Language Processing (NLP) scenarios, especially in machine translation.
Transformer based Grapheme-to-Phoneme Conversion
The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks
The aim of this paper is to introduce two widely applicable regularization methods based on the direct modification of weight matrices.
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another.
