Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper
Query: Information Encoding by Deep Neural Networks: What Can We Learn?
Opening the Black Box of Deep Neural Networks via Information
A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding
Deep Learning in Neural Networks: An Overview
Compressing Neural Networks using the Variational Information Bottleneck
Exploring strategies for training deep neural networks
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle
Understanding the difficulty of training deep feedforward neural networks
Learning the Number of Neurons in Deep Networks
Sequencediscriminative training of deep neural networks
Online Deep Learning: Learning Deep Neural Networks on the Fly
Reshaping deep neural network for fast decoding by node-pruning
Deep and Shallow Architecture of Multilayer Neural Networks
O N THE EXPRESSIVE POWER OF DEEP NEURAL NETWORKS
Auto-encoder bottleneck features using deep belief networks
Training and analyzing deep recurrent neural networks
a model for structured information representation in neural networks .
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Weighted-Entropy-Based Quantization for Deep Neural Networks
A survey of deep neural network architectures and their applications
Learning to cooperate in multi-agent systems by combining Q-learning and evolutionary strategy
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?
Fog computing through public-resource computing and storage
Development, Reliability, and Validity of the Handwriting Proficiency Screening Questionnaire (HPSQ)
Pupillary response: an index of visual threshold