Hierarchical 1D to 3D multiscale morph-tunable g-C3N4 assemblies were synthesized via a two-step transformation strategy for enhanced CO2 photoreduction.

Neural network language model (NN LM), such as long short term memory (LSTM) LM, has been increasingly popular due to its promising performance. However, the model size of an uncompressed NN LM is still too large to be used in embedded or portable devices. The dominant part of memory consumption of NN LM is the word embedding matrix. Directly compressing the word embedding matrix usually leads to performance degradation. In this paper, a product quantization based structured embedding approach is proposed to significantly reduce memory consumption of word embeddings without hurting LM performance. Here, each word embedding vector is cut into partial embedding vectors which are then quantized separately. Word embedding matrix can then be represented by an index vector and a code-book tensor of the quantized partial embedding vectors. Experiments show that the proposed approach can achieve 10 to 20 times embedding parameter reduction rate with negligible performance loss.

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