Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

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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 ...

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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 ...

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Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and magnetic field strength, making disruptions one of the key challenges for futur ...

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically ...

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 ...

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface compared to black- ...

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

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 ...

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation ...

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, ...

The increasing deployment of multiple deep neural networks (DNNs) on edge devices is revolutionizing mobile vision applications, spanning autonomous vehicles, augmented reality, and video surveillance. These applications demand adaptation to contextual and ...

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 ...

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. An extreme gradient boos ...

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Chattez avec Graph Search

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

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

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

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

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

Learning to Reason with Neural Networks: Principles and Methods

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Plasma state monitoring and disruption characterization using multimodal VAEs

From classification to segmentation with explainable AI: A study on crack detection and growth monitoring

Understanding generalization and robustness in modern deep learning

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting

High-performance deep spiking neural networks with 0.3 spikes per neuron

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

Towards the Detection of AI-Synthesized Human Face Images

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

Patching in Order: Efficient On-Device Model Fine-Tuning for Multi-DNN Vision Applications

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

Leveraging large language models for predictive chemistry

A generalized machine learning framework to estimate fatigue life across materials with minimal data