Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

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This paper studies the problem of learning computable functions in the limit by extending Gold's inductive inference framework to incorporate computational observations and restricted input sources. Complimentary to the traditional Input-Output Observation ...

We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs t ...

Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local fi ...

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: https://openaccess.thecvf.com/conte ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

Transformer-based models have revolutionized NLP. But in general, these models are highly resource consuming. Based on this consideration, several reservoir computing approaches to NLP have shown promising results. In this context, we propose EsnTorch, a l ...

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, li ...

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

Field Programmable Gate Arrays (FPGAs) are often used for accelerating machine learning algorithms. These algorithms can be implemented by various architectures which may differ in performance, required area, power consumption and reliability. This paper p ...

This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such ...

Part I of this paper formulated a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. A diffusion strategy was devised ...

We propose BlockProp, a neural network training algorithm. Unlike backpropagation, it does not rely on direct top-to-bottom propagation of an error signal. Rather, by interpreting backpropagation as a constrained optimization problem we split the neural ne ...

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

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Learning Algorithms in the Limit

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Ground-based image deconvolution with Swin Transformer UNet

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Optimal performance of Graph Convolutional Networks on the Contextual Stochastic Block Model

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

Learning sparse features can lead to overfitting in neural networks

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

The Esntorch Library: Efficient Implementation of Transformer-based Echo State Networks

Brain networks subserving functional core processes of emotions identified with componential modeling

Persistent Homology with Improved Locality Information for more Effective Delineation

Runtime Replacement of Machine Learning Modules in Fpga-based Systems

ResLogit: A residual neural network logit model for data-driven choice modelling

Learning Over Multitask Graphs-Part II: Performance Analysis

Decoupling Backpropagation using Constrained Optimization Methods