11 accepted papers
Dynamic sequences with varying lengths have been widely used in the training of Transformer-based large language models (LLMs). However, current training frameworks adopt a pre-defined static parallel strategy for these sequences, causing neither communication-parallelization cancellation on short s
Rumor detection on social media has become an emerging topic. Traditional deep learning-based methods model rumors based on content, propagation structure, or user behavior, but these approaches are constrained by limited modeling capacity and insufficient training corpora. Recent studies have explo…
Transformer models, such as BERT, GPT, and ViT, have been applied to a wide range of areas in recent years, due to their efficacy. In order to improve the training efficiency of Transformer models, different distributed training approaches have been proposed, like Megatron-LM [8]. However, when mult…
Event Extraction (EE) is a challenging task that aims to extract structural event-related information from unstructured text. Traditional methods for EE depend on manual annotations, which are both expensive and scarce. Furthermore, the existing datasets mostly follow the long-tail distribution, sev…
Effective group recommendation systems play a pivotal role in enriching the information consumption of users from different groups. Existing group recommendation approaches face challenges such as the sparsity of the rating matrix and low specificity between user clusters, leading to cold-start issu…
Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over…
Temporal knowledge graph completion that predicts missing links for incomplete temporal knowledge graphs (TKG) is gaining increasing attention. Most existing works have achieved good results by incorporating time information into static knowledge graph embedding methods. However, they ignore the con…
Since the rapid dissemination of fake news brings a lot of negative effects on real society, automatic fake news detection has attracted increasing attention in recent years. In most circumstances, the fake news detection task is a multimodal problem that consists of textual and visual contents. Man…
Temporal knowledge graph (TKG) reasoning that infers missing facts in the future is an essential and challenging task. When predicting a future event, there must be a narrative evolutionary process composed of closely related historical facts to support the event's occurrence, namely fact precursors…
In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algo…
Stereo matching algorithms usually consist of four steps, including matching cost calculation, matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN-based methods only adopt CNN to solve parts of the four steps, or use different networks to deal with different step…