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Found 8 papers, 2 papers with code
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in many scientific and business-intelligence applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting.
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?
Automatically Batching Control-Intensive Programs for Modern Accelerators
We present a general approach to batching arbitrary computations for accelerators such as GPUs.
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Furthermore, the only previous approaches that apply mixture consistency use real-valued masks; mixture consistency has been ignored for complex-valued masks.
Sound Audio and Speech Processing
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.
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech
Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech.
