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Social Opinion Formation and Decision Making Under Communication Trends

Related publications (56)

TIP: Tangible e-Ink Paper Manipulatives for Classroom Orchestration

Pierre Dillenbourg, Wafa Monia Benkaouar Johal, Ayberk Özgür, Hala Khodr, Alex The Duy Tran

While digital tools are more and more used in classrooms, teachers' common practice remains to use photocopied paper documents to share and collect learning exercises from their students. With the Tangible e-Ink Paper (TIP) system, we aim to explore the us ...

ACM2019

Encapsulating Online Labs in Structured Learning Spaces

Denis Gillet, Juan Carlos Farah

This paper builds on the results of European research projects to support STEM education with online labs. We present Integrated Interaction Apps as a means to support learning activities centered on online labs. We then show how these apps, labs and other ...

2018

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Hadrien Hendrikx

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learni ...

EPFL2017

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Reinforcement learning: the effect of environment

Michael Herzog, He Xu

Reinforcement learning is a type of supervised learning, where reward is sparse and delayed. For example in chess, a series of moves is made until a sparse reward (win, loss) is issued, which makes it impossible to evaluate the value of a single move. Stil ...

2016

Learning from sparse codes

Pascal Frossard, Sofia Karygianni

In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure lea ...

Ieee2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

Bidirectional Learning of Handwriting Skill in Human-Robot Interaction

Aude Billard, Hang Yin, Ana Paiva

This paper describes the design of a robot agent and associated learning algorithms to help children in handwriting acquisition. The main issue lies in how to program a robot to obtain human-like handwriting and then exploit it to teach children. We propos ...

2015

On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

Ali H. Sayed

This paper carries out a detailed transient analysis of the learning behavior of multiagent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies ...

2015

Transfer Learning through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

In this work we study the binary transfer learning problem involving 10^2 -10^3 sources. We focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer lea ...

Idiap2015

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