Search Results for author:
Found 15 papers, 7 papers with code
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Fast and energy-efficient neuromorphic deep learning with first-spike times
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance.
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control.
SpikE: spike-based embeddings for multi-relational graph data
We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations.
Machine learning on knowledge graphs for context-aware security monitoring
Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophistication displayed by attackers in hiding their activity.
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Over the recent years, a multitude of different graph neural network architectures demonstrated ground-breaking performances in many learning tasks.
An energy-based model for neuro-symbolic reasoning on knowledge graphs
Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving.
Relational representation learning with spike trains
Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and industrial projects for, e. g., the design of spacecraft.
Neuro-symbolic computing with spiking neural networks
Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way.
Selected Trends in Artificial Intelligence for Space Applications
The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced.
Neuromorphic Computing and Sensing in Space
The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks.
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods
Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain.
Decision Making
Explainable Artificial Intelligence (XAI)
+3
Differentiable graph-structured models for inverse design of lattice materials
Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.
