Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

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.

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

The nitrogen-vacancy (NV) center in diamond is a powerful and versatile quantum sensor for diverse quantities. In particular, relaxometry (or T1), can be used to detect magnetic noise at the nanoscale. For experiments with single NV centers the analysis of ...

A method for optimizing memory access for database operations is provided. The method may include performing a first database operation by at least executing a first instruction stream associated with the first database operation. The first database opera ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

Chat with Graph Search

Residual-based attention in physics-informed neural networks

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Learning to Reason with Neural Networks: Principles and Methods

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Toward data-driven materials design: From atoms to pilot plants

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Brain dynamics uncovered using a machine-learning algorithm

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Sequential Reasoning with Socially Caused Beliefs

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Validating functional redundancy with mixed generative adversarial networks

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Enriching the Computational Toolbox for Organocatalysis

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Optimizing Data Processing for Nanodiamond Based Relaxometry

Determining an optimum quantity of interleaved instruction streams of defined coroutines

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Data-driven approaches for non-invasive cuffless blood pressure monitoring