TimeGPT in load forecasting: A large time series model perspective

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Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

This thesis investigates methods to construct more capable models more efficiently, focusing on two aspects: improved architectures and optimization. We examine principled architectural modifications that reduce computational costs or introduce features fo ...

Advances in generative models increase the need for sample quality assessment. To do so, previous methods rely on a pre-trained feature extractor to embed the generated samples and real samples into a common space for comparison. However, different feature ...

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Genetic variation plays a crucial role in human disease susceptibility and molecular pathophysiology. This thesis explores genetic determinants of disease using advanced computational approaches, including statistical modeling, network analysis, and deep l ...

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, oc ...

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a patch-based classifier. This patch classifier is trained to identify classes p ...

Differential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing mini ...

We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given ...

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

Adding explanations to audio deepfake detection (ADD) models will enable insights on the decision making process and thus boost their real-world application. In this paper, we propose a relevancy-based explainable AI (XAI) method to analyze the predictions ...

Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not ...

Wireless sensing offers an alternative to wearables for contactless monitoring of human activity and vital signs. However, most existing systems use bistatic setups, which suffer from phase imperfections due to unsynchronized clocks. Monostatic systems ove ...

This paper proposes and experimentally validates a grid-aware scheduling and control framework for Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of active distribution networks (ADNs). The framework consists of two stages. In the ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

TimeGPT in load forecasting: A large time series model perspective

Chattez avec Graph Search

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Enhanced Architectures and Optimization Methods for Efficient Language Modeling

Assessing Sample Quality via the Latent Space of Generative Models

Musculoskeletal motor control with reinforcement learning

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Learning the language of the genome: AI- and data-driven decoding of genetic variants and disease mechanisms

Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label Classifier

Quantum Differential Privacy in the Local Model

Ensemble learning for portfolio valuation and risk management

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

What Does an Audio Deepfake Detector Focus on? A Study in the Time Domain

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence

An SDR-Based Monostatic Wi-Fi System with Analog Self-Interference Cancellation for Sensing

Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks: Theory and Experimental Validation

Topics in statistical physics of high-dimensional machine learning

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction