Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Extracting deep bottleneck features using stacked auto-encoders
Auto-encoder bottleneck features using deep belief networks
improved bottleneck features using pretrained deep neural networks .
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Using Different Cost Functions to Train Stacked Auto-Encoders
Deep Kernelized Autoencoders
Unsupervised feature extraction with autoencoder trees
Unsupervised Deep Autoencoders for Feature Extraction with Educational Data
Stacked deep convolutional auto-encoders for emotion recognition from facial expressions
Scalable stacking and learning for building deep architectures
Learning Deep Representations Using Convolutional Auto-Encoders with Symmetric Skip Connections
Deep convolutional neural networks as generic feature extractors
Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features
Deconvolutional Networks for Feature Learning
Supervised representation learning: transfer learning with deep autoencoders
Analyzing noise in autoencoders and deep networks
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
What is Deep Learning ? Multi-layer representation methods to learn features from data
classification of human activity by using a stacked autoencoder .
Transforming Autoencoders
Perceptual linear predictive (PLP) analysis of speech
Audible blink in prostaglandin-associated periorbitopathy
Compiling probabilistic logic programs into sentential decision diagrams
Blossom: A Tensile Social Robot Design with a Handcrafted Shell
Trait anxiety and impoverished prefrontal control of attention