Zobrazeno 1 - 10
of 404
pro vyhledávání: '"Hoi, Steven C. H."'
Autor:
Tiong, Anthony Meng Huat, Zhao, Junqi, Li, Boyang, Li, Junnan, Hoi, Steven C. H., Xiong, Caiming
Vision-language (VL) models, pretrained on colossal image-text datasets, have attained broad VL competence that is difficult to evaluate. A common belief is that a small number of VL skills underlie the variety of VL tests. In this paper, we perform
Externí odkaz:
http://arxiv.org/abs/2404.02415
Autor:
Yu, Xingtong, Fang, Yuan, Liu, Zemin, Wu, Yuxia, Wen, Zhihao, Bo, Jianyuan, Zhang, Xinming, Hoi, Steven C. H.
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily rely on the availability of ample labeled data. This constraint has spu
Externí odkaz:
http://arxiv.org/abs/2402.01440
Automatic program repair (APR) is crucial to reduce manual debugging efforts for developers and improve software reliability. While conventional search-based techniques typically rely on heuristic rules or a redundancy assumption to mine fix patterns
Externí odkaz:
http://arxiv.org/abs/2309.06057
Autor:
Liu, Chenghao, Yang, Wenzhuo, Mittal, Himanshu, Singh, Manpreet, Sahoo, Doyen, Hoi, Steven C. H.
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric causal dependencies and automati
Externí odkaz:
http://arxiv.org/abs/2306.11417
Dynamic Time Warping (DTW) has become the pragmatic choice for measuring distance between time series. However, it suffers from unavoidable quadratic time complexity when the optimal alignment matrix needs to be computed exactly. This hinders its use
Externí odkaz:
http://arxiv.org/abs/2306.00620
Autor:
Bui, Nghi D. Q., Le, Hung, Wang, Yue, Li, Junnan, Gotmare, Akhilesh Deepak, Hoi, Steven C. H.
Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leverag
Externí odkaz:
http://arxiv.org/abs/2306.00029
Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we intr
Externí odkaz:
http://arxiv.org/abs/2305.14720
Autor:
Wang, Yue, Le, Hung, Gotmare, Akhilesh Deepak, Bui, Nghi D. Q., Li, Junnan, Hoi, Steven C. H.
Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specifi
Externí odkaz:
http://arxiv.org/abs/2305.07922
Autor:
Cheng, Qian, Sahoo, Doyen, Saha, Amrita, Yang, Wenzhuo, Liu, Chenghao, Woo, Gerald, Singh, Manpreet, Saverese, Silvio, Hoi, Steven C. H.
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availa
Externí odkaz:
http://arxiv.org/abs/2304.04661
Autor:
Guo, Jiaxian, Li, Junnan, Li, Dongxu, Tiong, Anthony Meng Huat, Li, Boyang, Tao, Dacheng, Hoi, Steven C. H.
Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnec
Externí odkaz:
http://arxiv.org/abs/2212.10846