Future prediction is a fundamental principle of intelligence that helps plan actions and avoid
possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and …

Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …

Summary The Chemical Master Equation (CME) provides an accurate description of
stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved …

Tactile servoing is an important technique because it enables robots to manipulate objects
with precision and accuracy while adapting to changes in their environments in real-time …

This work presents a novel approach to address a challenging and still unsolved problem of
neural network based load forecasting systems, that despite the significant results reached …

Objective Accurate estimations of surgical case durations can lead to the cost-effective
utilization of operating rooms. We developed a novel machine learning approach, using …

Representing robot skills as movement primitives (MPs) that can be learned from human
demonstration and adapted to new tasks and situations is a promising approach toward …

Autonomous vehicles operating in urban environments need to deal with an abundance of
other dynamic objects, such as pedestrians and vehicles. This requires the development of …

Energy-based models (EBMs) have experienced a resurgence within machine learning in
recent years, including as a promising alternative for probabilistic regression. However …

The thermal and convective evolution of terrestrial planets like Mars is governed by a
number of initial conditions and parameters, which are poorly constrained. We use Mixture …