Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

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The escalating demands of artificial intelligence have underscored the limitations of traditional electronic computing, particularly in terms of power consumption and scalability. Optical computing offers a promising alternative, providing advantages in sp ...

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array ...

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 ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Urban proximity planning is foreseen as a solution to foster a “sustainable” city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks ...

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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 upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

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 ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling of interpretable partial feature effects while capturing higher-order non-linearities at the same time. A significant challe ...

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Chattez avec Graph Search

Programming Nonlinear Computation with Nonlinear and Linear Optics

Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems

Topics in statistical physics of high-dimensional machine learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

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

SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Multilingual Vision-Language Pre-training for the Remote Sensing Domain

Robust machine learning for neuroscientific inference

Random matrix methods for high-dimensional machine learning models

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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

Safe Deep Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Machine Learning for Modeling Stock Returns

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

A Functional Extension of Semi-Structured Networks