Neural architecture search (NAS) has shown the strong performance of learning neural models automatically in recent years. But most NAS systems are unreliable due to the architecture gap brought by discrete representations of atomic architectures. In this article, we improve the performance and robustness of NAS via narrowing the gap between architecture representations. More specifically, we apply a general contraction mapping to model neural networks with distributed representations (Neural Architecture Search with Distributed Architecture Representations (ArchDAR)). Moreover, for a better search result, we present a joint learning approach to integrating distributed representations with advanced architecture search methods. We implement our ArchDAR in a differentiable architecture search model and test learned architectures on the language modeling task. On the Penn Treebank data, it outperforms a strong baseline significantly by 1.8 perplexity scores. Also, the search process with distributed representations is more stable, which yields a faster structural convergence when it works with the differentiable architecture search model.

This paper proposes a verification of dual circularly polarized diversity mobile satellite communication system. We illustrate the statistical modeling of dual circularly polarized multiple-input-multiple-output(MIMO) channels which is based on the LOO model. Moreover, a point-to-point communication testing system is built with two USRPs and polarized-antennas. The results of the field test show that compared with SISO system, MIMO system can obtain an average signal-to-noise ratio(SNR) improvement of 2.43dB at 2.4kbps, and 1.99dB at 384kbps, respectively.

Neural Machine Translation (NMT) systems are undesirably slow as the decoder often has to compute probability distributions over large target vocabularies. In this work, we propose a coarse-to-fine approach to reduce the complexity of the decoding process, using only the information of the weight matrix in the Softmax layer. The large target vocabulary is first trimmed to a small candidate set in the coarse-grained phase, and from this candidate set the final top- k results are generated in the fine-grained phase. Tested on an RNN-based NMT system and a Transformer-based NMT system separately, our GPU-friendly method achieved a significant speed-up without harming the translation quality.

This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem.Previous methods require numerous training examples to estimate the accurate performance of architectures, although the actual goal is to find the distinction between "good" and "bad" candidates.Here we do not resort to performance predictors.Instead, we propose a performance ranking method (RankNAS) via pairwise ranking.It enables efficient architecture search using much fewer training examples.Moreover, we develop an architecture selection method to prune the search space and concentrate on more promising candidates.Extensive experiments on machine translation and language modeling tasks show that RankNAS can design high-performance architectures while being orders of magnitude faster than state-ofthe-art NAS systems.

Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.In this paper, we extend the search space of NAS.In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS).For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously.We implement our model in a differentiable architecture search system.For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and Wiki-Text data, with a new state-of-the-art on PTB.Moreover, the learned architectures show good transferability to other systems.E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale prelearned architectures.

Recently, the Transformer machine translation system has shown strong results by stacking attention layers on both the source and target-language sides. But the inference of this model is slow due to the heavy use of dot-product attention in auto-regressive decoding. In this paper we speed up Transformer via a fast and lightweight attention model. More specifically, we share attention weights in adjacent layers and enable the efficient re-use of hidden states in a vertical manner. Moreover, the sharing policy can be jointly learned with the MT model. We test our approach on ten WMT and NIST OpenMT tasks. Experimental results show that it yields an average of 1.3X speed-up (with almost no decrease in BLEU) on top of a state-of-the-art implementation that has already adopted a cache for fast inference. Also, our approach obtains a 1.8X speed-up when it works with the AAN model. This is even 16 times faster than the baseline with no use of the attention cache.

Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.