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Found 4 papers, 1 papers with code
Benchmarking and Enhancing Disentanglement in Concept-Residual Models
Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features, i. e., concepts, from observations that are subsequently used to condition a downstream task.
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning
The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics induced in multi-agent settings.
Deep Interpretable Models of Theory of Mind
When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand.
SlimNets: An Exploration of Deep Model Compression and Acceleration
We show that by combining pruning and knowledge distillation methods we can create a compressed network 85 times smaller than the original, all while retaining 96% of the original model's accuracy.
