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.

Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures. We argue for the computational advantages of a recurrent architecture with complex-valued weights. We propose a fully convolutional autoencoder, SynCx, that performs iterative constraint satisfaction: at each iteration, a hidden layer bottleneck encodes statistically regular configurations of features in particular phase relationships; over iterations, local constraints propagate and the model converges to a globally consistent configuration of phase assignments. Binding is achieved simply by the matrix-vector product operation between complex-valued weights and activations, without the need for additional mechanisms that have been incorporated into current synchrony-based models. SynCx outperforms or is strongly competitive with current models for unsupervised object discovery. SynCx also avoids certain systematic grouping errors of current models, such as the inability to separate similarly colored objects without additional supervision.

This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithms in this setting. We make two hypotheses: 1) the environment evolves continuously with a bounded evolution rate; 2) a current model is known at each decision epoch but not its evolution. Our contribution can be presented in four points.

This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform noise distribution, and always running a fixed number of MCMC steps. After generating synthesized examples, we then update the model parameters according to the maximum likelihood learning gradient, as if the synthesized examples are fair samples from the current model. We treat this non-convergent short-run MCMC as a learned generator model or a flow model. We provide arguments for treating the learned non-convergent short-run MCMC as a valid model. We show that the learned short-run MCMC is capable of generating realistic images. More interestingly, unlike traditional EBM or MCMC, the learned short-run MCMC is capable of reconstructing observed images and interpolating between images, like generator or flow models. The code can be found in the Appendix.

We provide the pseudocode of our BMU-MoCo in Algorithm 1. Algorithm 1 Pseudocode of BMU-MoCo. The R@5 results and its corresponding FR/HM are reported. The memory data are simply used as training samples in the training process. The model architecture is exactly the same as Base-MoCo. Collecting highly parallel data for paraphrase evaluation.

We then compute the gradient of CKD w.r.t z