Search Results for author:
Found 10 papers, 5 papers with code
Decoding Natural Images from EEG for Object Recognition
This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition.
Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation
Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model.
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface
The cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive.
Group-wise Contrastive Learning for Neural Dialogue Generation
Neural dialogue response generation has gained much popularity in recent years.
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
Adaptive Parameterization for Neural Dialogue Generation
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction
In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features.
