1.3k citations · 1.7k across the 8 of their papers we have counts for
9 papers
Flamingo: a Visual Language Model for Few-Shot Learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc +24
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Fl…
Step-unrolled Denoising Autoencoders for Text Generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski +2
In this paper we propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising diffusion…
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Skanda Koppula, Victor Bapst, Marc Huertas-Company +15
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end,…
End-to-End Adversarial Text-to-Speech
Jeff Donahue, Sander Dieleman, Mikołaj Bińkowski +2
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on th…
High Fidelity Speech Synthesis with Adversarial Networks
Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman +5
Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in…
Batch weight for domain adaptation with mass shift
Mikołaj Bińkowski, R Devon Hjelm, Aaron Courville
Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution. Current solutions unsupervised domain…
On gradient regularizers for MMD GANs
Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski +1
We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD)…
Demystifying MMD GANs
Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel +1
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribu…
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikołaj Bińkowski, Gautier Marti, Philippe Donnat
We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspire…