Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: A Minimalistic Approach to Sum-Product Network Learning for Real Applications
Sum-product networks: A new deep architecture
NISP: Pruning Networks Using Neuron Importance Score Propagation
Smallify: Learning Network Size while Training
A Riemannian Network for SPD Matrix Learning
BAYESIAN LEARNING FOR NEURAL NETWORKS
Hierarchical Spatial Sum–Product Networks for Action Recognition in Still Images
Neural Variational Inference and Learning in Belief Networks
An Overview of Multi-Task Learning in Deep Neural Networks
Reinforced backpropagation for deep neural network learning
Structure Learning for Deep Neural Networks Based on Multiobjective Optimization
Neural Simpletrons: Learning in the Limit of Few Labels with Directed Generative Networks
Factor Graphs and the Sum Product Algorithm
Learning the Structure of Dynamic Probabilistic Networks
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
Text Understanding with the Attention Sum Reader Network
Learning and development in neural networks: the importance of starting small
learning with bayesian networks .
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Bayesian Network Learning with Parameter Constraints
Cross-sectional imaging of nodal metastases in the abdomen and pelvis
6-DOF Multi-session Visual SLAM using Anchor Nodes
Supply chain management: theory, practice and future challenges
Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning
Compiling Bayesian Networks Using Variable Elimination