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In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint …
The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, …
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive …
Acquiring count annotations generally requires less human effort than point-level and bounding box annotations. Thus, we propose the …
Instance segmentation methods often require costly per-pixel labels. We propose a method called WISE-Net that only requires point-level …
Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships …
Few-shot classification is challenging because the data distribution of the training set can be widely different to the test set as …
Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary …