Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Accumulating knowledge to tackle new tasks without necessarily forgetting the old ones is a
hallmark of human-like intelligence. But the current dominant paradigm of machine learning …

The capability of learning and generalizing from very few samples successfully is a
noticeable demarcation separating artificial intelligence and human intelligence. Despite the …

Despite differing from the human language processing mechanism in implementation and
algorithms, current language models demonstrate remarkable human-like or surpassing …

Current language models are unable to quickly learn new concepts on the fly, often
requiring a more involved finetuning process to learn robustly. Prompting in-context is not …

遥感影像分类与识别是近年来深度学习以及图像分类与识别研究的热点ꎬ
其中一个关键问题是因样本数据集的数据较少而极易出现过拟合ꎮ 许多图像分类的模型和方法 …

摘要语言理解是认知科学和计算机科学交叉领域共同关心的问题, 但两个学科在选择具体研究
问题时却十分不同. 认知科学领域的研究侧重解析大脑的工作机制, 更多地关注于描述大脑对 …

摘要近年来, 随着深度神经网络的快速发展, 它在越来越多的领域中有了广泛的应用.
深度神经网络模型在处理有序列依赖关系的预测问题时, 需要利用之前学习到的信息进行记忆 …

To understand the algorithm that supports the human brain's language representation,
previous research has attempted to predict neural responses to linguistic stimuli using …