In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.
We introduce a novel approach to common-sense physical reasoning that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion

Extreme Classification Methods have become of paramount importance, particularly for Information Retrieval (IR) problems, owing to the development of smart algorithms that are scalable to industry challenges. One of the prime class of models that aim to solve the memory and speed challenge of extreme multi-label learning is Group Testing. Multi-label Group Testing (MLGT) methods construct label groups by grouping original labels either randomly or based on some similarity and then train smaller classifiers to first predict the groups and then recover the original label vectors. Recently, a novel approach called MACH (Merged Average Classifiers via Hashing) was proposed which projects the huge label vectors to a small and manageable count-min sketch (CMS) matrix and then learns to predict this matrix to recover the original prediction probabilities. Thereby, the model memory scales O(logK) for K classes. MACH is a simple algorithm which works exceptionally well in practice. Despite this simplicity of MACH, there is a big gap between the theoretical understanding of the trade-offs with MACH. In this paper we fill this gap. Leveraging the theory of count-min sketch we provide precise quantification of the memory-identifiablity tradeoffs. We extend the theory to the case of multi-label classification, where the dependencies make the estimators hard to calculate in closed forms. To mitigate this issue, we propose novel quadratic approximation using the Inclusion-Exclusion Principle. Our estimator has significantly lower reconstruction error than the typical CMS estimator across various values of number of classes K, label sparsity and compression ratio.
How to estimate original probability vector for millions of classes from count-min sketch measurements - a theoretical and practical setup.

All living organisms struggle against the forces of nature to carve out niches where
they can maintain relative stasis. We propose that such a search for order amidst
chaos might offer a unifying principle for the emergence of useful behaviors in
artificial agents. We formalize this idea into an unsupervised reinforcement learning
method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the
objective of maximizing the probability of observed states under a model trained on
all previously seen states. The resulting agents acquire several proactive behaviors
to seek and maintain stable states such as balancing and damage avoidance, that
are closely tied to the affordances of the environment and its prevailing sources
of entropy, such as winds, earthquakes, and other agents. We demonstrate that
our surprise minimizing agents can successfully play Tetris, Doom, and control
a humanoid to avoid falls, without any task-specific reward supervision. We
further show that SMiRL can be used as an unsupervised pre-training objective
that substantially accelerates subsequent reward-driven learning
Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy.