Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as ...

Unmanned Aerial Vehicle (UAV) Cross-View Geo-Localization (CVGL) poses significant challenges due to the substantial view discrepancies between oblique UAV images and overhead satellite images. Existing methods heavily rely on supervised learning with labe ...

Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

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 ...

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the nega ...

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-p ...

Customer Satisfaction (CS) in call centers influences customer loyalty and the company's reputation. Traditionally, CS evaluations were conducted manually or with classical machine learning algorithms; however, advancements in deep learning have led to aut ...

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ...

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

We introduce the concept of "universal"password model - a password model that, once pre-trained, can automatically adapt its guessing strategy based on the target system. To achieve this, the model does not need to access any plaintext passwords from the t ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

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 ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Chat with Graph Search

Mitigating imaging systematics for DESI 2024 emission Line Galaxies and beyond

Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering

Stochastic pairwise preference convergence in Bayesian agents

Social Opinion Formation and Decision Making Under Communication Trends

Towards practical reinforcement learning for tokamak magnetic control

Network-based kinetic models: Emergence of a statistical description of the graph topology

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Stabilization of nonlinear systems by neural Lyapunov approximators and Sontag's formula

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

Cross-transfer Knowledge between Speech and Text Encoders to Evaluate Customer Satisfaction

Augmenting large language models with chemistry tools

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Networked Signal and Information Processing: Learning by multiagent systems

Predicting the long-term collective behaviour of fish pairs with deep learning

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

Discovering Influencers in Opinion Formation Over Social Graphs

Topics in statistical physics of high-dimensional machine learning

Impact of Redundancy on Resilience in Distributed Optimization and Learning