Please do not distribute the following outside of class.
Project guidelines and deliverables can be found here.
Out-of-Sample Recognition and Confidence
Two Sample Testing with Density Estimation
Better Features with Distributions
Structured Density Estimators
Density Based Cycle GAN
Auto-encoder Guided Models
Better Conditionals and Transformations
TAN VAEs
Density Models in RL
Gaussian Autoencoders
Training Tractable Models
Better Architectures for Set Embeddings
Set Embeddings and Point Cloud Models
Wavelets and Other Bases for Distributions
Set PCA
Differentiable Computing Collection Scan
Predictive Embeddings
Graph Autoencoder
Graph Generative Model
Statistical Graph Unit
Set as Graphs
Graphs as Sets
Graph Node Embedding Distribution
End-to-End Semantic ZSL
Neighborhood Enhanced Minibatches
Hybrid kNN Models
Hybrid Kernel Density
Knowledge Bases for ML
Multimodal Graphs
Multimodal Embeddings and Generative Models
Word Embeddings as Side Information in Generative Models
Distribution Based Sequential Modeling
Simple RNNs and SRU+{GRU,LSTM}