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Found 29 papers, 16 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?
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.
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.
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples.
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is underconstrained.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
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.
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
Humans do not acquire perceptual abilities in the way we train machines.
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
Simple, Distributed, and Accelerated Probabilistic Programming
For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips.
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker.
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody.
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
On Using Backpropagation for Speech Texture Generation and Voice Conversion
Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances.
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #2 on
Speech Synthesis
on North American English
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks
We demonstrate the effectiveness of our algorithm by successfully training large ImageNet models (Inception-V3, Resnet-50, Resnet-101 and Inception-Resnet-V2) with mini-batch sizes of up to 32000 with no loss in validation error relative to current baselines, and no increase in the total number of steps.
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.
Unsupervised Learning of Semantic Audio Representations
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds.
Ranked #41 on
Audio Classification
on AudioSet
Uncovering Latent Style Factors for Expressive Speech Synthesis
Prosodic modeling is a core problem in speech synthesis.
Fixing a Broken ELBO
Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models.
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.
Ranked #5 on
Speech Synthesis
on North American English
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.
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.
CNN Architectures for Large-Scale Audio Classification
Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio.
Scalable Learning of Non-Decomposable Objectives
Modern retrieval systems are often driven by an underlying machine learning model.
Trainable Frontend For Robust and Far-Field Keyword Spotting
Robust and far-field speech recognition is critical to enable true hands-free communication.
