Search Results for author:
Found 29 papers, 18 papers with code
Efficient Neural Audio Synthesis
The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.
Colorization Transformer
We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention.
Ranked #2 on
Colorization
on ImageNet val
A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems.
Tensor2Tensor for Neural Machine Translation
Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
Ranked #1 on
Speech Synthesis
on Mandarin Chinese
Conditional Image Generation with PixelCNN Decoders
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
Ranked #7 on
Density Estimation
on CIFAR-10
Deep Learning Based Text Classification: A Comprehensive Review
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.
Pixel Recurrent Neural Networks
Modeling the distribution of natural images is a landmark problem in unsupervised learning.
Ranked #5 on
Image Generation
on Binarized MNIST
Axial Attention in Multidimensional Transformers
We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors.
Ranked #26 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
Neural Machine Translation in Linear Time
The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source sequence and the other to decode the target sequence.
Ranked #1 on
Machine Translation
on WMT2015 English-German
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
Associative Long Short-Term Memory
We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system.
Video Pixel Networks
The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Grid Long Short-Term Memory
This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images.
A Convolutional Neural Network for Modelling Sentences
The ability to accurately represent sentences is central to language understanding.
Ranked #48 on
Natural Language Inference
on SNLI
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
It has been shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution, self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.
Parallel Multiscale Autoregressive Density Estimation
Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images.
Ranked #2 on
Image Compression
on ImageNet32
Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition.
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval.
Recurrent Convolutional Neural Networks for Discourse Compositionality
The compositionality of meaning extends beyond the single sentence.
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
To address the latter challenge, we propose to use Multidimensional Upscaling to grow an image in both size and depth via intermediate stages utilising distinct SPNs.
Ranked #2 on
Image Generation
on CelebA 256x256
Bayesian Inference for Large Scale Image Classification
We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.
Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and developed separately.
Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent
It is shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution; self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.
Do better ImageNet classifiers assess perceptual similarity better?
Perceptual distances between images, as measured in the space of pre-trained deep features, have outperformed prior low-level, pixel-based metrics on assessing perceptual similarity.
Deep Learning for Day Forecasts from Sparse Observations
The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages.
