Just Sort It! A Simple and Effective Approach to Active Preference Learning

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Recent years witnessed the development of powerful generative models based on flows, diffusion, or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical anal ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg(2) over 5 yr to constrain the cosmic expansion history through precise measurements of baryon acoustic oscillations (BAO). The scientific program for DESI ...

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements freq ...

Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements ...

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 COVID-19 pandemic has led to a significant increase in working from home worldwide, making the workfrom-home (WFH) setting a crucial context for studying the influence of indoor environmental quality (IEQ) on workers' well-being and productivity. A nar ...

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

The recent geopolitical conflicts in Europe highlighted the sensibility of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy system configurations to ensure energy supply stability, it is necess ...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

We present the first detection of the baryon acoustic oscillations (BAOs) signal obtained using unblinded data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sam ...

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Chat with Graph Search

Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective

Gibbs sampling the posterior of neural networks

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Identification of typical district configurations: A two-step global sensitivity analysis framework

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

Automated all-functionals infrared and Raman spectra

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Graph generative deep learning models with an application to circuit topologies

A state-of-the-art, systematic review of indoor environmental quality studies in work-from-home settings

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Identification of typical district configurations: A two-step global sensitivity analysis framework

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

First detection of the BAO signal from early DESI data

Center-aware Adversarial Augmentation for Single Domain Generalization