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Found 9 papers, 5 papers with code
Splicing Up Your Predictions with RNA Contrastive Learning
In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete.
PixelGAN Autoencoders
In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Ranked #9 on
Unsupervised Image Classification
on MNIST
Generative Adversarial Network
Unsupervised Image Classification
+1
Adversarial Autoencoders
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
Ranked #6 on
Unsupervised Image Classification
on MNIST
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks.
Learning Wake-Sleep Recurrent Attention Models
Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations.
Winner-Take-All Autoencoders
In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion.
Training Restricted Boltzmann Machine by Perturbation
During the learning, to produce a sample from the current model, we start from a training data and descend in the energy landscape of the "perturbed model", for a fixed number of steps, or until a local optima is reached.
k-Sparse Autoencoders
Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks.
Adaptive dropout for training deep neural networks
For example, our model achieves 5. 8% error on the NORB test set, which is better than state-of-the-art results obtained using convolutional architectures. "
