Dependable learning and control through Neural ODEs

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We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

Self-organization and emergent behavior lie at the core of many natural phenomena, from pattern formation in biological tissues to the collective motion of flocking birds. Harnessing these principles in computational models can unlock powerful approaches f ...

The simulation of many-body open quantum systems is key to solving numerous outstanding problems in physics, chemistry, material science, and in the development of quantum technologies. Near-term quantum computers may bring considerable advantage for the e ...

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied ran ...

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

This paper presents a detailed comparative analysis of two data-driven probabilistic forecasting methods - a density-based Empirical Copula (EC) and a non-parametric Quantile Regression (QR) - in the context of load forecasting for day-ahead market applica ...

Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks. Yet, the extent to which DNNs capture fundamental aspects of human vision such ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robus ...

This paper investigates the use of relaxed recentered logarithmic barrier functions in the context of nonlinear model predictive control. These functions are a variation of the regular log-barrier functions that are introduced in the objective function of ...

The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granul ...

The dynamical properties of water molecules confinedin the unidirectionalhydrophilic nanopores of AlPO4-54 are investigated withquasi-elastic neutron scattering as a function of temperature downto 118 K. AlPO4-54 has among the largest pores known foralumin ...

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Dependable learning and control through Neural ODEs

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Controlled learning of pointwise nonlinearities in neural-network-like architectures

Texture Synthesis using Neural Cellular Automata

Low-Rank Variational Quantum Algorithm for the Dynamics of Open Quantum Systems

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Deterministic equivalent and error universality of deep random features learning

Multi-Agent Learning in Contextual Games under Unknown Constraints

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

A Comparative Analysis of Empirical Copula and Quantile Regression Methods for Probabilistic Load Forecasting

Divergences in color perception between deep neural networks and humans

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans

Relaxed Recentered Log-Barrier Function based Nonlinear Model Predictive Control

Data-driven optimization tool for the functional, economic, and environmental properties of blended cement concrete using supplementary cementitious materials

Low-Temperature Dynamics of Water Confined in Unidirectional Hydrophilic Zeolite Nanopores

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming