8 accepted papers
Current backdoor attack defenders in Natural Language Processing (NLP) typically involve data reduction or model pruning, risking losing crucial information. To address this challenge, we introduce a novel backdoor defender, i.e., BadWindtunnel, in which we build a high-noise simulated training envi…
Deep time series anomaly detection (TSAD) essentially relies on learning data "normality". Current approaches leverage various neural network architectures, including RNNs, CNNs, Transformers, and graph neural networks, effectively modeling temporal and inter-variable dependencies within time series…
Depth completion is a popular research direction in the field of depth estimation. The fusion of color and depth features is the critical challenge in this task, mainly due to the asymmetry between the rich scene details in color images and the sparse pixels in depth maps. To tackle this issue, we d…
Large language models (LLMs) generate human-aligned content under certain safety constraints. However, the current known technique “jailbreak prompt” can circumvent safety-aligned measures and induce LLMs to output malicious content. Research on Jailbreaking can help identify vulnerabilities in LLMs
Link prediction in directed graphs offers a solution for uncovering detailed and accurate relationships among distinct entities. Unlike conventional link prediction in undirected graphs, the task becomes more intricate in directed graphs as it involves predicting both associations and orientations.…
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a ch…
Multimodal rumor detection aims at detecting rumors using information from textual and visual modalities. The most critical difficulty in multimodal rumor detection lies in capturing both the intra-modal and inter-modal relationships from multimodal data. However, existing methods mainly focus on th…
The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only th…