Zobrazeno 1 - 10
of 206
pro vyhledávání: '"Hooi, Bryan"'
Autor:
Wang, Huilin, Hooi, Bryan
Phishing detection is a critical cybersecurity task that involves the identification and neutralization of fraudulent attempts to obtain sensitive information, thereby safeguarding individuals and organizations from data breaches and financial loss.
Externí odkaz:
http://arxiv.org/abs/2408.01667
Contrastive Language-Image Pre-training (CLIP) achieves remarkable performance in various downstream tasks through the alignment of image and text input embeddings and holds great promise for anomaly detection. However, our empirical experiments show
Externí odkaz:
http://arxiv.org/abs/2407.17083
Graph pre-training has been concentrated on graph-level on small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial scenarios,
Externí odkaz:
http://arxiv.org/abs/2407.03953
Autor:
Anand, Rishabh, Joshi, Chaitanya K., Morehead, Alex, Jamasb, Arian R., Harris, Charles, Mathis, Simon V., Didi, Kieran, Hooi, Bryan, Liò, Pietro
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA
Externí odkaz:
http://arxiv.org/abs/2406.13839
Autor:
Liu, Juncheng, Liu, Chenghao, Woo, Gerald, Wang, Yiwei, Hooi, Bryan, Xiong, Caiming, Sahoo, Doyen
Transformer-based models have emerged as powerful tools for multivariate time series forecasting (MTSF). However, existing Transformer models often fall short of capturing both intricate dependencies across variate and temporal dimensions in MTS data
Externí odkaz:
http://arxiv.org/abs/2406.04975
While large language models (LLMs) have achieved significant success in various applications, they often struggle with hallucinations, especially in scenarios that require deep and responsible reasoning. These issues could be partially mitigate by in
Externí odkaz:
http://arxiv.org/abs/2405.13873
Autor:
Li, Yuexin, Huang, Chengyu, Deng, Shumin, Lock, Mei Lin, Cao, Tri, Oo, Nay, Lim, Hoon Wei, Hooi, Bryan
Phishing attacks have inflicted substantial losses on individuals and businesses alike, necessitating the development of robust and efficient automated phishing detection approaches. Reference-based phishing detectors (RBPDs), which compare the logos
Externí odkaz:
http://arxiv.org/abs/2403.02253
Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality. Current approaches often rely on the model's tok
Externí odkaz:
http://arxiv.org/abs/2402.15300
Autor:
He, Yufei, Hooi, Bryan
Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications beyond their initial training objectives. However, when this concept is
Externí odkaz:
http://arxiv.org/abs/2402.13630
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data. However, a major challenge that hinders both unimodal and multimodal contrastive learning is feature suppression, a phenomenon whe
Externí odkaz:
http://arxiv.org/abs/2402.11816