Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization framework, GCNs perform an implicit Laplacian type regularization to …
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4 results for “LCNs”
CNNs require fewer samples than LCNs and FCNs for image-based tasks due to locality and weight sharing.
problem Quantifying statistical benefits of locality and weight sharing in CNNs over LCNs and FCNs.
method Dynamic Signal Distribution (DSD) task and information theoretic tools.
result CNNs require fewer samples than LCNs and FCNs for image-based tasks.
Theoretical analysis of CNNs' inductive biases and their efficiency in approximating functions.
problem Understanding and optimizing the inductive biases in deep CNNs.
method Theoretical analysis combining multichanneling, downsampling, weight sharing, and locality.
result Deep CNNs with depth can approximate any continuous function, and require samples for sparse functions.
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and Morecs.LG
Two log-linear approximations speed up optimal transport for deep learning applications.
problem Computing optimal transport in high dimensions is computationally expensive.
method Locality-sensitive hashing (LSH) and Nyström approximation with LSH-based sparse corrections.
result Log-linear time algorithms for entropy-regularized OT perform well in high-dimensional spaces.