Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

To explain these observations, we visualize how label smoothing changes therepresentations learned bythepenultimate layerofthenetwork. We show that label smoothing encourages the representations of training examples from thesame class togroup intight clusters. This results inloss ofinformation inthe logits about resemblances between instances ofdifferent classes, which isnecessary for distillation, but does not hurt generalization or calibration of the model'spredictions.

Subsequently, LfV oid trains an ensembled goal discriminator on the generated image to provide reward signals for a reinforcement learning agent, guiding it to achieve the goal.

We study the problem of how to repeatedly sell to a buyer running a no-regret,mean-based algorithm. Previous work [Braverman et al., 2018] shows that it ispossible to design effective mechanisms in such a setting that extract almost allof the economic surplus, but these mechanisms require the buyer's values each

Unlike previous 3D datasets (e.g., Objaverse) which are often captured or crafted by humans to approximate real 3D data, Zeroverse completely ignores realistic global semantics but is

Adaptation Video-text Retrieval (UDA VR), assuming that training (source) data and testing (target) data are from different domains.

We study the problem of estimating ap-dimensional s-sparse vector in a linear model with Gaussian design and additivenoise.