Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning--from computing the expectations of decision tree ensembles to information-theoretic divergencesofsum-product networks--can berepresented interms oftractable modular operations overcircuits.

Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning.

However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers.

Based oninstance discrimination [14, 4, 6, 5, 13, 1], some methods show the effectiveness in the image classification task.