Search Results for author:
Found 13 papers, 3 papers with code
Semformer: Transformer Language Models with Semantic Planning
In this paper, we introduce Semformer, a novel method of training a Transformer language model that explicitly models the semantic planning of response.
Revealing the Parallel Multilingual Learning within Large Language Models
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities.
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack
Specifically, face images are masked in the frequency domain using an adaptive MixUp strategy.
Power Transformer Fault Prediction Based on Knowledge Graphs
In this paper, we address the challenge of learning with limited fault data for power transformers.
Single-pixel imaging based on deep learning
Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors.
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder
The natural language generation domain has witnessed great success thanks to Transformer models.
Audience Expansion for Multi-show Release Based on an Edge-prompted Heterogeneous Graph Network
In the offline stage, to construct the graph, user IDs and specific side information combinations of the shows are chosen to be the nodes, and click/co-click relations and view time are used to build the edges.
Enhanced Preamble Based MAC Mechanism for IIoT-oriented PLC Network
Finally, with our work, a PLC network based on E-PMAC mechanism can be realized.
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning ability
The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks.
Code-Switching for Enhancing NMT with Pre-Specified Translation
Leveraging user-provided translation to constrain NMT has practical significance.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
Improved English to Russian Translation by Neural Suffix Prediction
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.
