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cs.LG2022
Interactive Concept Bottleneck Models
Kushal Chauhan, Rishabh Tiwari, Jan Freyberg +2
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…
cs.LG2022
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
Barath Mohan Umapathi, Kushal Chauhan, Pradeep Shenoy +1
Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been lar…
cs.LG2022
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning
Kushal Chauhan, Soumya Chatterjee, Akash Reddy +2
The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstract…