Yet, despite the attractive properties of time-index based models, such as being a continuous signal function over time leading to smooth representations, little attention has been given to them.
Modern software systems rely on mining insights from business sensitive data stored in public clouds.
This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity.
We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras.
Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.
We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data.
Solving multiagent problems can be an uphill task due to uncertainty in the environment, partial observability, and scalability of the problem at hand.
However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources.
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment.
Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system.
We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers.
We experiment on the real-world protein design dataset and show that EM's convergence rate is significantly higher than the previous LP relaxation based approach MPLP.