SIMBa: System Identification Methods Leveraging Backpropagation

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost t ...

How does the brain generate behavioral responses from sensory stimuli? This sensor-to-motor transformation is a fundamental question in systems neuroscience. While recent experiments have highlighted the complexity of neural representations across the cere ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspectio ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition o ...

Non-linear partial differential Kolmogorov equations are successfully used to describe a wide range of time dependent phenomena, in natural sciences, engineering or even finance. For example, in physical systems, the Allen-Cahn equation describes pattern f ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data i ...

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

The drastic shift towards digital communication in our mediasphere has caused a profound change in the production and consumption of information, which in turn has substantial implications on the social and political landscape. Misinformation, as a side ef ...

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning h ...

We study how neural networks compress uninformative input space in models where data lie in d dimensions, but the labels of which only vary within a linear manifold of dimension d(parallel to) < d. We show that for a one-hidden-layer network initialized wi ...

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a system ...

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learnin ...

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

SIMBa: System Identification Methods Leveraging Backpropagation

Chattez avec Graph Search

Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

Parametrizations of all stable closed-loop responses: From theory to neural network control design

Capturing behavioral and neural variability in multi-area spiking neuronal network models

Reliable data-driven decision-making through optimal transport

Aggregating Spatial and Photometric Context for Photometric Stereo

Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Euclid: Identification of asteroid streaks in simulated images using deep learning

Solving Non-linear Kolmogorov Equations in Large Dimensions by Using Deep Learning: A Numerical Comparison of Discretization Schemes

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Combating Online Scientific Misinformation

Discrete-Choice Mining of Social Processes

Business Entity Matching with Siamese Graph Convolutional Networks

Geometric compression of invariant manifolds in neural networks

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Medical Image Classification Using Generalized Zero Shot Learning

Genuinely Distributed Byzantine Machine Learning