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Found 5 papers, 4 papers with code
Interactive Concept Bottleneck Models
Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
We also show that our solutions work well with other types of generative models (generative flows and variational autoencoders) and that their efficacy is governed by each model's reliance on local dependencies.
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning
The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space.
Robust outlier detection by de-biasing VAE likelihoods
Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data.
Improving Segmentation for Technical Support Problems
We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches.
