Jiatao Gu

TL;DR: CopyNet as discussed by the authors incorporates copying into neural network-based Seq2Seq learning and proposes a new model called CopyNet with encoder-decoder structure, which can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.

TL;DR: This paper incorporates copying into neural network-based Seq2Seq learning and proposes a new model called CopyNet with encoder-decoder structure which can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.

TL;DR: The authors use knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning to avoid the autoregressive property and produce its outputs in parallel, allowing an order of magnitude lower latency during inference.

TL;DR: This work introduces Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering that is over 10 times faster than the state-of-the-art (namely, NeRF) at inference time while achieving higher quality results.