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Found 28 papers, 11 papers with code
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information.
A Generative Product-of-Filters Model of Audio
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain.
Structured Stochastic Variational Inference
Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization.
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Beta process is the standard nonparametric Bayesian prior for latent factor model.
Image Classification and Retrieval from User-Supplied Tags
This paper proposes direct learning of image classification from user-supplied tags, without filtering.
A trust-region method for stochastic variational inference with applications to streaming data
However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization.
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important.
Mathematical Software G.1.0; G.1.3; G.1.4; F.2.1
A Variational Analysis of Stochastic Gradient Algorithms
With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution.
Deep Probabilistic Programming
By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning.
Stochastic Gradient Descent as Approximate Bayesian Inference
Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions.
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.
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Deep latent Gaussian models are powerful and popular probabilistic models of high-dimensional data.
Generalizing Hamiltonian Monte Carlo with Neural Networks
We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution.
Variational Autoencoders for Collaborative Filtering
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
Ranked #5 on
Recommendation Systems
on Million Song Dataset
Music Transformer
This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length.
Ranked #3 on
Music Modeling
on JSB Chorales
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.
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.
Automatic Reparameterisation of Probabilistic Programs
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data.
Langevin Dynamics as Nonparametric Variational Inference
Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased.
Automatically Batching Control-Intensive Programs for Modern Accelerators
We present a general approach to batching arbitrary computations for accelerators such as GPUs.
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
What Are Bayesian Neural Network Posteriors Really Like?
The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex.
Lossy Compression with Gaussian Diffusion
Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on the efficient communication of pixels corrupted by Gaussian noise.
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is underconstrained.
Sequential Monte Carlo Learning for Time Series Structure Discovery
This paper presents a new approach to automatically discovering accurate models of complex time series data.
Training Chain-of-Thought via Latent-Variable Inference
Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt.
Robust Inverse Graphics via Probabilistic Inference
How do we infer a 3D scene from a single image in the presence of corruptions like rain, snow or fog?
