Search Results for author: Brendan Frey

Found 9 papers, 5 papers with code

Splicing Up Your Predictions with RNA Contrastive Learning

1 code implementation12 Oct 2023 Philip Fradkin, Ruian Shi, Bo wang, Brendan Frey, Leo J. Lee

In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete.

Contrastive Learning Property Prediction +1

PixelGAN Autoencoders

1 code implementation NeurIPS 2017 Alireza Makhzani, Brendan Frey

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.

Decoder Generative Adversarial Network +2

Adversarial Autoencoders

28 code implementations18 Nov 2015 Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey

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.

Clustering Data Visualization +6

Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

no code implementations17 Nov 2015 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.

General Classification Image Classification +2

Learning Wake-Sleep Recurrent Attention Models

no code implementations NeurIPS 2015 Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey

Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations.

Caption Generation Computational Efficiency +2

Winner-Take-All Autoencoders

1 code implementation NeurIPS 2015 Alireza Makhzani, Brendan Frey

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

no code implementations6 May 2014 Siamak Ravanbakhsh, Russell Greiner, Brendan Frey

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

3 code implementations19 Dec 2013 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.

Classification Denoising +1

Adaptive dropout for training deep neural networks

no code implementations NeurIPS 2013 Jimmy Ba, Brendan Frey

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. "


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