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.

[Q]: In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users. Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss. The list is then sorted in the descending order of the scores. Possible interactions between items present in the same list are taken into account in the training phase at the loss level. However, during inference, items are scored individually, and possible interactions between them are not considered. In this paper, we propose a context-aware neural network model that learns item scores by applying a self-attention mechanism. The relevance of a given item is thus determined in the context of all other items present in the list, both in training and in inference. Finally, we empirically demonstrate significant performance gains of self-attention based neural architecture over Multi-Layer Perceptron baselines. This effect is consistent across popular pointwise, pairwise and listwise losses on datasets with both implicit and explicit relevance feedback.
[A]: Learning to rank using the Transformer architecture.


[Q]: We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.
[A]: We propose a formulation of intrinsic motivation that is suitable as an exploration bias in multi-agent sparse-reward synergistic tasks, by encouraging agents to affect the world in ways that would not be achieved if they were acting individually.


[Q]: We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%. The goal is to shed light on two well-known mysteries in deep neural networks: (i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent; (ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well. We performed extensive experiments on persistent homology of a range of point cloud data sets. The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers. No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero. Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers.
[A]:
We show that neural networks operate by changing topologly of a data set and explore how architectural choices effect this change.