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Artificial Neural Networks for Impact Position Detection in Haptic Surfaces

Related publications (46)

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PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Andreas Krause, Martin Josifoski

Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Demonstrator Surface For Machine Learning Algorithm

In the ﬁeld of haptic feedback, LAI is working on a way of localizing impact vibrations through machine learning algorithms. In this semester project, the goal is to extend a one-dimensional system into a two-dimensional system with a demonstrator surface ...

2021

Subspace-Based Learning for Automatic Dysarthric Speech Detection

Hervé Bourlard, Ina Kodrasi, Parvaneh Janbakhshi

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Fourier Sampling in Signal Processing and Numerical Linear Algebra

Amir Zandieh

This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...

EPFL2020

Adaptive hot water production based on Supervised Learning

Dolaana Khovalyg, Amirreza Heidari

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2020

Artificial Neural Network Approach to the Analytic Continuation Problem

Oleg Yazyev, Quansheng Wu, Lei Wang, Romain Fournier

Inverse problems are encountered in many domains of physics, with analytic continuation of the imaginary Green's function into the real frequency domain being a particularly important example. However, the analytic continuation problem is ill defined and c ...

AMER PHYSICAL SOC2020

A Machine-Learning Framework for Automating Well-Log Depth Matching

Timon Zimmermann

Depth matching well logs acquired from multiple logging passes in a single well has been a longstanding challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance m ...

SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA2019

MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems

Jan Sickmann Hesthaven, Deep Ray

Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satised to obtain ...

MATHICSE2019

Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings

The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while di-arizing new conversation ...

2019

Bridging the Gap between Semantics and Multimedia Processing

Roberto Gerson De Albuquerque Azevedo

In this paper, we overview the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the semantic gap in terms of a classical architecture for multimedia processing and discuss a ...

IEEE2019