Learning to control the structure of sentences is a challenging problem in text generation.

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved?

This work focuses on the representation learning question: how can we learn such features? Under the assumption that the underlying (unknown) dynamics correspond to a low rank transition matrix, we show how the representation learning question is related to a particular non-linear matrix decomposition problem.

A DAG is a directed graph with no directed paths starting and ending at the same node.

We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.

To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE).

Bayesian inference in the space of functions has been an important topic for Bayesian modeling in the past. In this paper, we propose a new solution to this problem called Functional V ariational Inference (FVI). In FVI, we minimize a divergence in function space between the variational distribution and the posterior process.

Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems. In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning.

ListNet for instance considers the predicted scores as parameters for the Plackett-Luce distribution [39, 40] and learns these scores via maximum likelihood estimation. Used in a PiRank surrogate loss of Section 3.1, the relaxation presented in Section 3.2 recovers the This finishes the proof by induction. Taking j = d, we obtain from Eq. 22 and the nature of permutation matrices that lim C14, we use "Set 1" which is the larger of the two provided For both datasets, we use the standard train/validation/test splits. The experiments were run on a server with 4 8-core Intel Xeon E5-2620v4 CPUs, 128 GB of RAM and 4 NVIDIA Telsa K80 GPUs. TensorFlow Ranking is licensed under the Apache License 2.0 MSLR-WEB30K is licensed under the Microsoft Research License Agreement (MSR-LA).