Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …

We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

In this paper we present a broad overview of the last 40 years of research on cognitive
architectures. To date, the number of existing architectures has reached several hundred …

Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each
pyramidal neuron receives input from thousands of excitatory synapses that are segregated …

Automatic log file analysis enables early detection of relevant incidents such as system
failures. In particular, self-learning anomaly detection techniques capture patterns in log …

Learning continuously during all model lifetime is fundamental to deploy machine learning
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …

Cognitive maps are mental representations of spatial and conceptual relationships in an
environment, and are critical for flexible behavior. To form these abstract maps, the …

With the development of 5G and Internet of Vehicles technology, the possibility of remote
wireless attack on an in-vehicle network has been proven by security researchers. Anomaly …