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Found 11 papers, 5 papers with code
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone.
The LORACs prior for VAEs: Letting the Trees Speak for the Data
In variational autoencoders, the prior on the latent codes $z$ is often treated as an afterthought, but the prior shapes the kind of latent representation that the model learns.
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images.
Model-based Reinforcement Learning
reinforcement-learning
+1
Estimating the Spectral Density of Large Implicit Matrices
However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors.
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professional-grade software applications are powerful but complicated$-$expert users can achieve impressive results, but novices often struggle to complete even basic tasks.
Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.
Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths.
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions.
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a. k. a.
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Rodent hippocampal population codes represent important spatial information about the environment during navigation.
