Dif-MAML: Decentralized Multi-Agent Meta-Learning

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The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn peri ...

This paper reflects on the pedagogy used for a series of student workshops on building with reused materials at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and presents the results of its 7 th edition, which focused on floor systems. During these w ...

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment i ...

Within the current context of globalisation, trasnationalism has acquired importance as a social phenomenon and an object of study, since it shows the value that the diasporas' own decentralized mechanisms have in promoting development in the countries of ...

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 ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

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 ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

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 ...

Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain critical pa ...

Dif-MAML: Decentralized Multi-Agent Meta-Learning

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Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

On-line learning and modulation of periodic movements with nonlinear dynamical systems

Pedagogy of an interdisciplinary, hands-on workshop to design and build floor systems with reused materials

Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

Transnational practices to benefit local science and technology: Empirical evidence of the Colombian scientific diaspora

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

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Prior knowledge in Kernel methods

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

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

A learning-based approach to stochastic optimal control under reach-avoid constraint

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

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

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

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

Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics