Prediction model of tunnel boring machine performance by ensemble neural networks

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This paper investigates the presence of OCR-sensitive neurons within the Transformer architecture and their influence on named entity recognition (NER) performance on historical documents. By analysing neuron activation patterns in response to clean and no ...

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. W ...

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

Cross-field transport of particles in the boundary region of magnetically confined fusion plasmas is dominated by turbulence. Blobs, intermittent turbulent structures with large amplitude and a filamentary shape appearing in the scrape-off layer (SOL), are ...

Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) e ...

The defective regimes in metal-based Laser Powder Bed Fusion (LPBF) processes can be minimized by deploying in-situ monitoring strategies comprising Machine learning (ML) algorithms and sensing techniques. So far, algorithms trained for monitoring a partic ...

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong predi ...

This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training ef ...

Learning-based algorithms have gained great popularity in communications since they often outperform even carefully engineered solutions by learning from training samples. In this paper, we show that the selection of appropriate training examples can be im ...

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes ...

Quality assessment of images is of key importance for media applications. In this paper we present a new objective metric to predict the quality of images using deep neural networks. The network makes use of both the color information as well as frequency ...

A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus o ...

Prediction model of tunnel boring machine performance by ensemble neural networks

Chattez avec Graph Search

Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents

On the impact of learning-based image compression on computer vision tasks

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Estimating cross-field particle transport at the outer midplane of TCV by tracking filaments with machine learning

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Robustness via Cross-Domain Ensembles

On Symmetry and Initialization for Neural Networks

Training Channel Selection for Learning-based 1-bit Precoding in Massive MU-MIMO

Detecting the Unexpected via Image Resynthesis

A new objective metric to predict image quality using deep neural networks

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks