text: 468,7,736213513484554241,70831441,Soumith Chintala,"""Discovering Causal Signals in Images"". Given an image, can you tell which objects cause others? Our new paper. wdyt?<LINK> @RahelJhirad why use simulated images when you dont need to... @RahelJhirad i think using videos is an easier proposition. The arrow of time is a strong signal. @RahelJhirad Either ways, training on image-specific data is not needed, as shown in our paper.",http://arxiv.org/abs/1605.08179,"This paper establishes the existence of observable footprints that reveal the ""causal dispositions"" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational causal discovery, and build a classifier that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution. Second, we use our causal direction classifier to effectively distinguish between features of objects and features of their contexts in collections of static images. Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects. ",Discovering Causal Signals in Images,4,"['""Discovering Causal Signals in Images"". Given an image, can you tell which objects cause others? Our new paper. wdyt?<LINK>', '@RahelJhirad why use simulated images when you dont need to...', '@RahelJhirad i think using videos is an easier proposition. The arrow of time is a strong signal.', '@RahelJhirad Either ways, training on image-specific data is not needed, as shown in our paper.']",16,05,380