Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

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Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynam ...

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

The creation of high fidelity synthetic data has long been an important goal in machine learning, particularly in fields like finance where the scarcity of available training and test data make it difficult to utilize many of the deep learning techniques w ...

Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approache ...

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

This work presents a novel hybrid (physics- and data -driven) model for short-term (intra-day and day-ahead, 3h -24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points close ...

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

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A comparative analysis of gene expression profiling by statistical and machine learning approaches

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Computational design of complex protein folds and soluble analogs of membrane proteins

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

Person Re-Identification and its Application to Multi-Object Tracking

Sequential Reasoning with Socially Caused Beliefs

Asymptotics of feature learning in two-layer networks after one gradient-step

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Understanding generalization and robustness in modern deep learning

Electronic Excited States from Physically Constrained Machine Learning

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes

Macroeconomic Conditioned Synthetic Financial Markets

On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space