3.6k citations · 10k across the 28 of their papers we have counts for
28 papers
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting
Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong +16
Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly…
Deep Learning for Day Forecasts from Sparse Observations
Marcin Andrychowicz, Lasse Espeholt, Di Li +5
Deep neural networks offer an alternative paradigm for modeling weather conditions. The ability of neural models to make a prediction in less than a second once the data is availab…
Do better ImageNet classifiers assess perceptual similarity better?
Manoj Kumar, Neil Houlsby, Nal Kalchbrenner +1
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 similarit…
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
Lasse Espeholt, Shreya Agrawal, Casper Sønderby +7
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among…
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
Samira Abnar, Rianne van den Berg, Golnaz Ghiasi +3
We focus on the problem of domain adaptation when the goal is shifting the model towards the target distribution, rather than learning domain invariant representations. It has been…
Colorization Transformer
Manoj Kumar, Dirk Weissenborn, Nal Kalchbrenner
We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds i…
Towards Causal Representation Learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer +4
The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit…
A Spectral Energy Distance for Parallel Speech Synthesis
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg +2
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…
MetNet: A Neural Weather Model for Precipitation Forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek +6
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuousl…
Axial Attention in Multidimensional Transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn +1
We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either su…
Bayesian Inference for Large Scale Image Classification
Jonathan Heek, Nal Kalchbrenner
Bayesian inference promises to ground and improve the performance of deep neural networks. It promises to be robust to overfitting, to simplify the training procedure and the space…
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
Jacob Menick, Nal Kalchbrenner
The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to genera…
Tensor2Tensor for Neural Machine Translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo +10
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…
Efficient Neural Audio Synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan +7
Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. E…
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aaron van den Oord, Yazhe Li, Igor Babuschkin +19
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…
Parallel Multiscale Autoregressive Density Estimation
Scott Reed, Aäron van den Oord, Nal Kalchbrenner +4
PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N…
Video Pixel Networks
Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan +4
We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural a…
Neural Machine Translation in Linear Time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan +3
We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source se…
WaveNet: A Generative Model for Raw Audio
Aaron van den Oord, Sander Dieleman, Heiga Zen +6
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for e…
Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals +3
This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive…
Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria +2
We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters. The system has an associative memory based o…
Pixel Recurrent Neural Networks
Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We…
Grid Long Short-Term Memory
Nal Kalchbrenner, Ivo Danihelka, Alex Graves
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…