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06/16/2023
Automatic Trade-off Adaptation in Offline RL
Recently, offline RL algorithms have been proposed that remain adaptive ...
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12/23/2022
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods
Machine learning (ML) on graph-structured data has recently received dee...
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11/15/2022
A Metaheuristic Approach for Mining Gradual Patterns
Swarm intelligence is a discipline that studies the collective behavior ...
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08/31/2022
Ant Colony Optimization for Mining Gradual Patterns
Gradual pattern extraction is a field in (KDD) Knowledge Discovery in Da...
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08/04/2022
Neuro-symbolic computing with spiking neural networks
Knowledge graphs are an expressive and widely used data structure due to...
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05/21/2022
User-Interactive Offline Reinforcement Learning
Offline reinforcement learning algorithms still lack trust in practice d...
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02/18/2022
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
Tiny machine learning (TinyML) has gained widespread popularity where ma...
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01/14/2022
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Offline reinforcement learning (RL) Algorithms are often designed with e...
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12/03/2021
Combining Sub-Symbolic and Symbolic Methods for Explainability
Similarly to other connectionist models, Graph Neural Networks (GNNs) la...
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11/26/2021
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
Recently developed offline reinforcement learning algorithms have made i...
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11/25/2021
Ontology-Based Skill Description Learning for Flexible Production Systems
The increasing importance of resource-efficient production entails that ...
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11/25/2021
Demystifying Graph Neural Network Explanations
Graph neural networks (GNNs) are quickly becoming the standard approach ...
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09/21/2021
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Computing latent representations for graph-structured data is an ubiquit...
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07/12/2021
Behavior Constraining in Weight Space for Offline Reinforcement Learning
In offline reinforcement learning, a policy needs to be learned from a s...
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05/04/2021
The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT
Focusing on comprehensive networking, big data, and artificial intellige...
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03/15/2021
TinyOL: TinyML with Online-Learning on Microcontrollers
Tiny machine learning (TinyML) is a fast-growing research area committed...
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08/12/2020
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning
State-of-the-art reinforcement learning algorithms mostly rely on being ...
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06/19/2020
Neural Topic Modeling with Continual Lifelong Learning
Lifelong learning has recently attracted attention in building machine l...
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12/23/2019
Visual Evaluation of Generative Adversarial Networks for Time Series Data
A crucial factor to trust Machine Learning (ML) algorithm decisions is a...
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09/29/2019
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
Topic models such as LDA, DocNADE, iDocNADEe have been popular in docume...
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09/13/2019
Neural Architectures for Fine-Grained Propaganda Detection in News
This paper describes our system (MIC-CIS) details and results of partici...
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07/10/2019
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
In this paper, we present a Bayesian view on model-based reinforcement l...
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10/16/2018
Multimodal Deep Gaussian Processes
We propose a novel Bayesian approach to modelling multimodal data genera...
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10/11/2018
Neural Relation Extraction Within and Across Sentence Boundaries
Past work in relation extraction mostly focuses on binary relation betwe...
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12/10/2017
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
We derive a novel sensitivity analysis of input variables for predictive...
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10/08/2017
Bayesian Alignments of Warped Multi-Output Gaussian Processes
We present a Bayesian extension to convolution processes which defines a...
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10/19/2016
