Robust Learning-Augmented Caching: An Experimental Study

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In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

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Robust Learning-Augmented Caching: An Experimental Study

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Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Socially Intelligent Networks: A framework for decision making over graphs

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

The multimodality cell segmentation challenge: toward universal solutions

Incorporating Projective Geometry into Deep Learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Statistical mechanics of deep learning

A ride time-oriented scheduling algorithm for dial-a-ride problems

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

On the Generalization of Stochastic Gradient Descent with Momentum

In-Sensor Passive Speech Classification with Phononic Metamaterials

Robust NAS under adversarial training: benchmark, theory, and beyond

Revisiting Character-level Adversarial Attacks for Language Models

Machine Learning for Modeling Stock Returns