459 citations · 1.1k across the 18 of their papers we have counts for
18 papers
Generating and designing DNA with deep generative models
Nathan Killoran, Leo J. Lee, Andrew Delong +2
We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using…
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey
Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due…
Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov +1
Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown…
Winner-Take-All Autoencoders
Alireza Makhzani, Brendan Frey
In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all a…
Training Restricted Boltzmann Machine by Perturbation
Siamak Ravanbakhsh, Russell Greiner, Brendan Frey
A new approach to maximum likelihood learning of discrete graphical models and RBM in particular is introduced. Our method, Perturb and Descend (PD) is inspired by two ideas (I) pe…
k-Sparse Autoencoders
Alireza Makhzani, Brendan Frey
Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods invol…