Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3Dobject detection architecture onpoint clouds.

Inspired by the neural mechanisms that may contribute to the brain's associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework.

Weimplement our approach using TensorFlow[1]forefficient GPU-based computation and also use some components of TensorFlow Probability [4]2.

We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.

In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.