Deep Learning
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Saliency maps are popular tools for explaining the decisions of convolutional neural networks (CNNs) for image classification.
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Thus, we restrict our search for a constrained clustering approach to the class of deep generative models. Although these models have been successfully used in the unsupervised setting (Jiang et al., 2017; Dilokthanakul et al., 2016), their application to constrained clustering has been under-explored.
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators
There are two types of deep generative models: explicit and implicit.
Appendix for TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets Chengrun Y ang 1, Gabriel Bender
Due to the high costs involved, many works have proposed different methods to reduce the search cost. The first strategy is to reduce the time needed to evaluate each architecture seen during a search. The second strategy is to reduce the number of architectures we need to evaluate during a search. Resource constraints are prevalent in deep learning. Finding architectures with outstanding performance and low costs are important to both NAS research and application.
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets Chengrun Y ang 1, Gabriel Bender
Previous NAS algorithms designed for image search spaces incorporate resource constraints directly into the reinforcement learning (RL) rewards.
