19,043 research outputs found
Variational Sequential Monte Carlo
Many recent advances in large scale probabilistic inference rely on
variational methods. The success of variational approaches depends on (i)
formulating a flexible parametric family of distributions, and (ii) optimizing
the parameters to find the member of this family that most closely approximates
the exact posterior. In this paper we present a new approximating family of
distributions, the variational sequential Monte Carlo (VSMC) family, and show
how to optimize it in variational inference. VSMC melds variational inference
(VI) and sequential Monte Carlo (SMC), providing practitioners with flexible,
accurate, and powerful Bayesian inference. The VSMC family is a variational
family that can approximate the posterior arbitrarily well, while still
allowing for efficient optimization of its parameters. We demonstrate its
utility on state space models, stochastic volatility models for financial data,
and deep Markov models of brain neural circuits
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
The Deep Weight Prior
Bayesian inference is known to provide a general framework for incorporating
prior knowledge or specific properties into machine learning models via
carefully choosing a prior distribution. In this work, we propose a new type of
prior distributions for convolutional neural networks, deep weight prior (DWP),
that exploit generative models to encourage a specific structure of trained
convolutional filters e.g., spatial correlations of weights. We define DWP in
the form of an implicit distribution and propose a method for variational
inference with such type of implicit priors. In experiments, we show that DWP
improves the performance of Bayesian neural networks when training data are
limited, and initialization of weights with samples from DWP accelerates
training of conventional convolutional neural networks.Comment: TL;DR: The deep weight prior learns a generative model for kernels of
convolutional neural networks, that acts as a prior distribution while
training on new dataset
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