168 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- KTH Royal Institute of TechnologySE17 papers
- Chalmers University of TechnologySE15 papers
- Lund UniversitySE13 papers
- Luleå University of TechnologySE8 papers
- University of California, BerkeleyUS8 papers
- Blekinge Institute of TechnologySE6 papers
- Uppsala UniversitySE6 papers
- National Physical LaboratoryGB5 papers
- University of GothenburgSE5 papers
- Intel (United States)US4 papers
- Istituto Nazionale di Ricerca MetrologicaIT4 papers
- Physikalisch-Technische BundesanstaltDE4 papers
17 papers · 1 filter
Multiclass Linear Perceptrons with Multiplicative Margins
Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov +2
This paper introduces a family of multiclass linear Perceptron classifiers with a multiplicative margin mechanism (MMPerc), as an alternative to standard margin-free and additive m…
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
Abhishek Srinivasan, Varun Singapuri Ravi, Juan Carlos Andresen +1
The complexity of modern electro-mechanical systems require the development of sophisticated diagnostic methods like anomaly detection capable of detecting deviations. Conventional…
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
Ahbishek Srinivasan, Juan Carlos Andresen, Anders Holst
A core part of maintenance planning is a monitoring system that provides a good prognosis on health and degradation, often expressed as remaining useful life (RUL). Most of the cur…
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Shenghui Li, Edith C. -H. Ngai, Thiemo Voigt
Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants may upload arbitrary local updates to…
Identifying latent distances with Finslerian geometry
Alison Pouplin, David Eklund, Carl Henrik Ek +1
Riemannian geometry provides us with powerful tools to explore the latent space of generative models while preserving the underlying structure of the data. The latent space can be…
On-device Training: A First Overview on Existing Systems
Shuai Zhu, Thiemo Voigt, JeongGil Ko +1
The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. Whi…
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Jens Henriksson, Christian Berger, Markus Borg +3
Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achieve…
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami +1
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of th…
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
Antonello Rosato, Massimo Panella, Evgeny Osipov +1
A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary tra…
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing
Cameron Diao, Denis Kleyko, Jan M. Rabaey +1
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored f…
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Jens Henriksson, Christian Berger, Markus Borg +3
Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applica…
Auto-weighted Robust Federated Learning with Corrupted Data Sources
Shenghui Li, Edith Ngai, Fanghua Ye +1
Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their…
Perceptron Theory Can Predict the Accuracy of Neural Networks
Denis Kleyko, Antonello Rosato, E. Paxon Frady +2
Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzin…
Online feature selection for rapid, low-overhead learning in networked systems
Xiaoxuan Wang, Forough Shahab Samani, Rolf Stadler
Data-driven functions for operation and management often require measurements collected through monitoring for model training and prediction. The number of data sources can be very…
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez +1
Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in man…
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Andreas Vogelsang, Markus Borg
Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requiremen…
Towards Structured Evaluation of Deep Neural Network Supervisors
Jens Henriksson, Christian Berger, Markus Borg +4
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applicatio…