Paraphrase generation aims to synthesize paraphrases of a given sentence automatically. We use the official splits to report our results. Thus, there are 6513, 497 and 2,990 video clips in trainingset,validationsetandtestset,respectively. Following theSelf-Critical Sequence Training [24](SCST), thegradient ofLRL(θ)canbe approximatedby θLRL(θ) (r(ys1:T) r(ˆy1:T)) θlogpθ(ys1:T) (3) where r(ys1:T) is the score of a sampled captionys1:T and r(ˆy1:T) suggests the baseline score of a caption which is generated by the current model using greedy decode. Distilling knowledge learned inBERTfortext generation. A deep generative framework for paraphrase generation.

However,foreachtimestepinthedecoding process, theattention based models usually use the hidden state of the current input to attend to the image regions.

However, existing ways of implementing the ME-HP for such data are either inflexible, as the exogenous (background) rate functions are typically constant and the endogenous (excitation) rate functions are specified parametrically, or inefficient, as inference usually relies on Markov chain Monte Carlo methods with high computational costs.

It then serves as probabilistic prior to regularize the inverse problem(1).

Toachievethis, webuildonInfluenceFunction(IF)andproposesource-awareIF 2,whichleverages the generation process of the probabilistic labels to decompose the end model's training objective and then calculate the influence associated with each (data, source, class)tuple.

Although tremendous success has been achieved in spatial and network representation separately in recent years, there exist very little works on the representation of spatial networks. Extracting powerful representations from spatial networks requires the development of appropriate tools to uncover the pairing of both spatial and network information in the appearance of node permutation invariant, and rotation and translation invariant. Hence it can not be modeled merely with either spatial or network models individually. To address these challenges, this paper proposes a generic framework for spatial network representation learning. Specifically, a provably information-lossless and rotation-translation invariant representation of spatial information on networks is presented. Then a higher-order spatial network convolution operation that adapts to our proposed representation is introduced. To ensure efficiency, we also propose a new approach that relied on sampling random spanning trees to reduce the time and space complexity fromO(N3) to O(N).

In this work, we propose a new approach to R-D estimation from the perspective of optimal transport.