Fault-tolerant architectures for nanoelectronic circuits employing simple feed-forward neural networks without learning

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The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Adaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on Electroencephalograph ...

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most ...

We propose Grab-UCB, a graph-kernelmulti-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learni ...

To make social robots effective in education, they need to be autonomous both in terms of assessing the student's engagement state as well as intervening effectively in soft real-time when necessary. Hidden Markov Model (HMM) is an interpretable machine le ...

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students’ interaction data ...

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range an ...

The Robots for Learning workshop series aims at advancing the research topics related to the use of social robots in educational contexts. This year's half-day workshop follows on previous events in Human-Robot Interaction conferences focusing on efforts t ...

In this paper, we propose a novel methodology and design to contribute towards the achievement of the 17 Sustainable Development Goals (SDGs) adopted by member states of the United Nations for a better and more sustainable future for all. We particularly f ...

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

Synthesis from examples enables non-expert users to generate programs by specifying examples of their behavior. A domain-specific form of such synthesis has been recently deployed in a widely used spreadsheet software product. In this paper we contribute t ...

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 ...

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem i ...

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Perceptual learning is learning to see. For example in a bisection task, three parallel lines are presented with the central line slightly offset towards the right or the left outer line. Participants indicate the offset direction. Training gradually impro ...

This article introduces learning analytics dashboards that visualize learning traces for learners and teachers. We present a conceptual framework that helps to analyze learning analytics applications for these kinds of users. We then present our own work i ...

Fault-tolerant architectures for nanoelectronic circuits employing simple feed-forward neural networks without learning

Chattez avec Graph Search

Prediction rigidities for data-driven chemistry

EEG-Based Online Regulation of Difficulty in Simulated Flying

Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning

Online Network Source Optimization with Graph-Kernel MAB

An HMM-based Real-time Intervention Methodology for a Social Robot Supporting Learning

Generalisable Methods for Early Prediction in Interactive Simulations for Education

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Scalable Robust Graph Embedding with Spark

Graph Laplacian Mixture Model

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

Robots for Learning - R4L: Adaptive Learning

On the Design of a Youth-Led, Issue-Based, Crowdsourced Global Monitoring Framework for the SDGs

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Proactive Synthesis of Recursive Tree-to-String Functions from Examples

Theory of representation learning in cortical neural networks

SVM Active Learning Approach for Image Classification Using Spatial Information

Learning with tensors: a framework based on convex optimization and spectral regularization

Perceptual Learning With Only One Stimulus

Learning Analytics Dashboard Applications