Zobrazeno 1 - 10
of 632
pro vyhledávání: '"Li, Alexander"'
Autor:
Khesin, Andrey Boris, Li, Alexander
Quantum error-correcting codes (QECC's) are needed to combat the inherent noise affecting quantum processes. Using ZX calculus, we represent QECC's in a form called a ZX diagram, consisting of a tensor network. In this paper, we present canonical for
Externí odkaz:
http://arxiv.org/abs/2406.12083
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
Autor:
Li, Alexander Hanbo, Shang, Mingyue, Spiliopoulou, Evangelia, Ma, Jie, Ng, Patrick, Wang, Zhiguo, Min, Bonan, Wang, William, McKeown, Kathleen, Castelli, Vittorio, Roth, Dan, Xiang, Bing
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, z
Externí odkaz:
http://arxiv.org/abs/2308.05317
Autor:
Fu, Xingyu, Zhang, Sheng, Kwon, Gukyeong, Perera, Pramuditha, Zhu, Henghui, Zhang, Yuhao, Li, Alexander Hanbo, Wang, William Yang, Wang, Zhiguo, Castelli, Vittorio, Ng, Patrick, Roth, Dan, Xiang, Bing
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to the task and shown
Externí odkaz:
http://arxiv.org/abs/2305.18842
Autor:
Wang, Sijia, Li, Alexander Hanbo, Zhu, Henry, Zhang, Sheng, Hang, Chung-Wei, Perera, Pramuditha, Ma, Jie, Wang, William, Wang, Zhiguo, Castelli, Vittorio, Xiang, Bing, Ng, Patrick
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality configuration, such as text-only EL, visual grounding, or schema linki
Externí odkaz:
http://arxiv.org/abs/2305.17337
Autor:
Lan, Wuwei, Wang, Zhiguo, Chauhan, Anuj, Zhu, Henghui, Li, Alexander, Guo, Jiang, Zhang, Sheng, Hang, Chung-Wei, Lilien, Joseph, Hu, Yiqun, Pan, Lin, Dong, Mingwen, Wang, Jun, Jiang, Jiarong, Ash, Stephen, Castelli, Vittorio, Ng, Patrick, Xiang, Bing
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively evaluate text-to-SQL systems, we introduce a UNIfied benchmark for Text
Externí odkaz:
http://arxiv.org/abs/2305.16265
The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional ge
Externí odkaz:
http://arxiv.org/abs/2303.16203
Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the
Externí odkaz:
http://arxiv.org/abs/2302.14051
Autor:
Chang, Shuaichen, Wang, Jun, Dong, Mingwen, Pan, Lin, Zhu, Henghui, Li, Alexander Hanbo, Lan, Wuwei, Zhang, Sheng, Jiang, Jiarong, Lilien, Joseph, Ash, Steve, Wang, William Yang, Wang, Zhiguo, Castelli, Vittorio, Ng, Patrick, Xiang, Bing
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustne
Externí odkaz:
http://arxiv.org/abs/2301.08881
Autor:
Zhao, Yiyun, Jiang, Jiarong, Hu, Yiqun, Lan, Wuwei, Zhu, Henry, Chauhan, Anuj, Li, Alexander, Pan, Lin, Wang, Jun, Hang, Chung-Wei, Zhang, Sheng, Dong, Marvin, Lilien, Joe, Ng, Patrick, Wang, Zhiguo, Castelli, Vittorio, Xiang, Bing
Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further
Externí odkaz:
http://arxiv.org/abs/2212.08785