Training Traffic Light Behavior with End-to-End Learning

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Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as ...

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the nega ...

In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, ...

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Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques ...

Current speech recognition architectures perform very well from the point of view of machine learning, hence user interaction. This suggests that they are emulating the human biological system well. We investigate whether the inference can be inverted to p ...

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire ...

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. ...

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In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

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This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation by convolutional neural network (CNN) methods. For easy adaptation and deployment of the system, we train the CNN using purely s ...

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

Objective. The vestibular system provides essential information about balance and spatial orientation via the brain to other sensory and motor systems. Bilateral vestibular loss significantly reduces quality of life, but vestibular implants (VIs) have demo ...

Perception of a visual target can strongly deteriorate in the presence of flanking elements (crowding). For example, adding lines next to a vernier makes vernier offset discrimination difficult. Crowding is often considered a bottleneck of low-level vision ...

Training Traffic Light Behavior with End-to-End Learning

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Mitigating imaging systematics for DESI 2024 emission Line Galaxies and beyond

Stabilization of nonlinear systems by neural Lyapunov approximators and Sontag's formula

Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Investigation of effective processes parameters on lead (II) adsorption from wastewater by biochar in mild air oxidation pyrolysis process

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Low-Level Physiological Implications of End-to-End Learning of Speech Recognition

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Reverse engineering the motor control system

Computational memory-based inference and training of deep neural networks

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Learning Lightprobes for Mixed Reality Illumination

Deep Convolutional Neural Network for Inverse Problems in Imaging

Word Sequence Modeling using Deep Learning : an End-to-end Approach and its Applications

Characterization of pulse amplitude and pulse rate modulation for a human vestibular implant during acute electrical stimulation

Targets but not flankers are suppressed in crowding as revealed by EEG frequency tagging