15 citations · 32 across the 10 of their papers we have counts for
12 papers
Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques
Leila Bagheriye, Johan Kwisthout
Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-…
Speeding up approximate MAP by applying domain knowledge about relevant variables
Johan Kwisthout, Andrew Schroeder
The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving…
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
Charles Theodore Kent, Leila Bagheriye, Johan Kwisthout
Recent strides in the field of neural computation has seen the adoption of Winner Take All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiki…
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data
Mark Peelen, Leila Bagheriye, Johan Kwisthout
The integration of multi-omics data has emerged as a promising approach for gaining comprehensive insights into complex diseases such as cancer. This paper proposes a novel approac…
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
Otto van der Himst, Leila Bagheriye, Johan Kwisthout
Winner Take All (WTA) circuits a type of Spiking Neural Networks (SNN) have been suggested as facilitating the brain's ability to process information in a Bayesian manner. Research…
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
Dominique J. Kösters, Bryan A. Kortman, Irem Boybat +10
The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performanc…
Motivating explanations in Bayesian networks using MAP-independence
Johan Kwisthout
In decision support systems the motivation and justification of the system's diagnosis or classification is crucial for the acceptance of the system by the human user. In Bayesian…
Parameterized Complexity Results for Bayesian Inference
Hans Bodlaender, Nils Donselaar, Johan Kwisthout
We present completeness results for inference in Bayesian networks with respect to two different parameterizations, namely the number of variables and the topological vertex separa…
On the computational power and complexity of Spiking Neural Networks
Johan Kwisthout, Nils Donselaar
The last decade has seen the rise of neuromorphic architectures based on artificial spiking neural networks, such as the SpiNNaker, TrueNorth, and Loihi systems. The massive parall…
A spiking neural algorithm for the Network Flow problem
Abdullahi Ali, Johan Kwisthout
It is currently not clear what the potential is of neuromorphic hardware beyond machine learning and neuroscience. In this project, a problem is investigated that is inherently dif…
Finding dissimilar explanations in Bayesian networks: Complexity results
Johan Kwisthout
Finding the most probable explanation for observed variables in a Bayesian network is a notoriously intractable problem, particularly if there are hidden variables in the network.…
The Computational Complexity of Sensitivity Analysis and Parameter Tuning
Johan Kwisthout, Linda C. van der Gaag
While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact computa…