text: 69,219,1281306072930750465,881959726958862337,Yuhuai (Tony) Wu,"Can Neural Networks solve IQ tests? We propose Scattering Compositional Learner (SCL) for RPM Task. SCL improves SOTA from 63.9% to 95.0%. It is even capable of zero-shot generalization and learns disentangled representations! paper: <LINK> (1/n) <LINK> SCL is designed to discover the compositional structures of the data. In RAVEN, It learns to discover the compositions of objects, attributes, and relationships. The figure shows an example where SCL learns the concept of “size”. (2/n) <LINK> By learning compositional structures, it can even generalize to unseen analogies. E.g., After learning (“color”, “constant”), and (“size”, “progression”), the model can generalize to (“color”, “progression”). (3/n) Fun fact: Hu et. al. (<LINK>) found that most of the previous successful neural methods exploited a short-cut solution. After removing the dataset bias, those methods suffered a lot (e.g., CoPINet went from 91.4% -&gt; 46.3%). SCL was not affected at all. (4/n) Last but not the least, this is a joint work with Honghua Dong, @RogerGrosse, and Jimmy Ba. @marbin2050 @cjmaddison Thanks for encouraging words. We're exploring all potential of this work. @FelixHill84 Hi Felix, many thanks for encouraging words! PGM is a dataset of much larger scale, so we were not able to run the task and compare it with baselines by the deadline. But we are intending to try for sure! @iandanforth Hi Ian, thanks for pointing out the Abstraction and Reasoning Challenge. We will take a closer look to see if our model fits!",https://arxiv.org/abs/2007.04212,"In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM). To discover compositional structures of the data, we propose the Scattering Compositional Learner (SCL), an architecture that composes neural networks in a sequence. Our SCL achieves state-of-the-art performance on two RPM datasets, with a 48.7% relative improvement on Balanced-RAVEN and 26.4% on PGM over the previous state-of-the-art. We additionally show that our model discovers compositional representations of objects' attributes (e.g., shape color, size), and their relationships (e.g., progression, union). We also find that the compositional representation makes the SCL significantly more robust to test-time domain shifts and greatly improves zero-shot generalization to previously unseen analogies. ","The Scattering Compositional Learner: Discovering Objects, Attributes,