Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

The LSTM network was proposed to overcome the difficulty in learning long-term
dependence, and has made significant advancements in applications. With its success and …

Abstract Language models based on recurrent neural networks have dominated recent
image caption generation tasks. In this paper, we introduce a Language CNN model which …

Geometric deep learning is a relatively nascent field that has attracted significant attention in
the past few years. This is partly due to the availability of data acquired from non-euclidean …

Deep reinforcement learning (RL) has shown immense potential for learning to control
systems through data alone. However, one challenge deep RL faces is that the full state of …

In this paper, we propose a novel multimodal fusion framework, called the locally confined
modality fusion network (LMFN), that contains a bidirectional multiconnected LSTM (BM …

Causal discovery from time-series data has been a central task in machine learning.
Recently, Granger causality inference is gaining momentum due to its good explainability …

Modelling long-term dependencies is a challenge for recurrent neural networks. This is
primarily due to the fact that gradients vanish during training, as the sequence length …

Multimodal human sentiment comprehension refers to recognizing human affection from
multiple modalities. There exist two key issues for this problem. First, it is difficult to explore …