How Machine Learning is challenged by algorithmic complexity classes. From models to practices

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Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could ...

This paper assesses the feasibility of estimating the position and attitude of a target satellite using computer vision, not during proximity operations, but during conjunctions at distances greater than 1 km. The selected target is a small 5 × 5 × 5 cm3 P ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed thr ...

In this thesis we present three closed form approximation methods for portfolio valuation and risk management. The first chapter is titled ``Kernel methods for portfolio valuation and risk management'', and is a joint work with Damir Filipovi'c (SFI and ...

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

In the CORTEX project, methods to simulate neutron flux oscillations were enhanced and machine-learning based tools to determine the causes of measured neutron flux oscillations were developed, using the results of simulations as training and validation da ...

Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called ...

In this paper, we introduce our recent studies on human perception in audio event classification. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by and used as feature extractor for o ...

The main focus of this research is the development of real-time recording and analysis devices and algorithms for detecting Electroencephalography (EEG) correlates of steering actions. First, offline analysis where performed in a high fidelity driving simu ...

In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems (DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In ...

Background: Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for ...

How Machine Learning is challenged by algorithmic complexity classes. From models to practices

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Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers

Tiny satellites, big challenges: A feasibility study of machine vision pose estimation for PocketQubes during conjunctions

Graph generative deep learning models with an application to circuit topologies

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Estimation of horizontal running power using foot-worn inertial measurement units

Closed form approximation methods for portfolio valuation and risk management

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Sensitivity analysis in core diagnostics

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Deep Learning of Human Perception in Audio Event Classification

Detecting EEG correlates during preparation of complex driving maneuvers

Learning Motion Dynamics to Catch a Moving Object

Refining transcriptional regulatory networks using network evolutionary models and gene histories