Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

This paper investigates the joint localization, detection, and tracking of sound events using a
convolutional recurrent neural network (CRNN). We use a CRNN previously proposed for …

Many sequential classification tasks are affected by label noise that varies over time. Such
noise can cause label quality to improve, worsen, or periodically change over time. We first …

Recursive Bayesian estimation builds on the use of suitable mathematical models in order to
accurately describe the evolution of an object based on imperfect sensor data. Sequential …

The majority of current research on reinforcement learning (RL) for snake robot control do
not sufficiently account for the spatial and temporal dependencies within the robot or its …

In multitarget tracking, finding an association between the new measurements and the
known targets is a crucial challenge. By considering both the uncertainties of all the …

Deep learning methods have recently been used to construct non-linear codes for the
additive white Gaussian noise (AWGN) channel with feedback. However, there is limited …

Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic
interpretation of probabilistic graphical models (PGMs). In this paper, we propose an …

An important feature of human sensorimotor skill is our ability to learn to reuse them across
different environmental contexts, in part due to our understanding of attributes of variability in …

Many sequential classification tasks are affected by label noise that changes over time. Such
noise might arise from label quality improving, worsening, or periodically changing over …