Zobrazeno 1 - 10
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pro vyhledávání: '"Pham, Hai"'
Autor:
Padlewski, Piotr, Bain, Max, Henderson, Matthew, Zhu, Zhongkai, Relan, Nishant, Pham, Hai, Ong, Donovan, Aleksiev, Kaloyan, Ormazabal, Aitor, Phua, Samuel, Yeo, Ethan, Lamprecht, Eugenie, Liu, Qi, Wang, Yuqi, Chen, Eric, Fu, Deyu, Li, Lei, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Artetxe, Mikel, Tay, Yi
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. Vib
Externí odkaz:
http://arxiv.org/abs/2405.02287
Autor:
Reka Team, Ormazabal, Aitor, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Fu, Deyu, Ong, Donovan, Chen, Eric, Lamprecht, Eugenie, Pham, Hai, Ong, Isaac, Aleksiev, Kaloyan, Li, Lei, Henderson, Matthew, Bain, Max, Artetxe, Mikel, Relan, Nishant, Padlewski, Piotr, Liu, Qi, Chen, Ren, Phua, Samuel, Yang, Yazheng, Tay, Yi, Wang, Yuqi, Zhu, Zhongkai, Xie, Zhihui
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of t
Externí odkaz:
http://arxiv.org/abs/2404.12387
Autor:
Pham, Hai X., Hadji, Isma, Xu, Xinnuo, Degutyte, Ziedune, Rainey, Jay, Kazakos, Evangelos, Fazly, Afsaneh, Tzimiropoulos, Georgios, Martinez, Brais
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to train compact (e.g., run on a mobile device), task-specific QA models th
Externí odkaz:
http://arxiv.org/abs/2401.13594
Autor:
Pham, Hai, Kim, Young Jin, Mukherjee, Subhabrata, Woodruff, David P., Poczos, Barnabas, Awadalla, Hany Hassan
Mixture-of-experts (MoE) architecture has been proven a powerful method for diverse tasks in training deep models in many applications. However, current MoE implementations are task agnostic, treating all tokens from different tasks in the same manne
Externí odkaz:
http://arxiv.org/abs/2308.15772
Due to the prohibitively high cost of creating error correction datasets, most Factual Claim Correction methods rely on a powerful verification model to guide the correction process. This leads to a significant drop in performance in domains like sci
Externí odkaz:
http://arxiv.org/abs/2305.14707
Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood. In this paper, we develop a novel framework to study th
Externí odkaz:
http://arxiv.org/abs/2211.03970
Autor:
Dvornik, Nikita, Hadji, Isma, Pham, Hai, Bhatt, Dhaivat, Martinez, Brais, Fazly, Afsaneh, Jepson, Allan D.
Publikováno v:
ECCV 2022
In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute
Externí odkaz:
http://arxiv.org/abs/2210.04996
Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input. Most current transformer-b
Externí odkaz:
http://arxiv.org/abs/2208.08201
Autor:
Pham, Trung Dinh, Bach, Bao Gia, Luu, Lam Trinh, Nguyen, Minh Dinh, Pham, Hai Duc, Anh, Khoa Bui, Nguyen, Xuan Quang, Quoc, Cuong Pham
Publikováno v:
Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, 2022
Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step t
Externí odkaz:
http://arxiv.org/abs/2206.04520
Autor:
Duong, An Thi Binh, Diep, Uyen My, Sampaio, Paulo, Carvalho, Maria, Pham, Hai Thanh, Hoang, Thu-Hang, Truong, Dung Quang, Truong, Huy Quang
Publikováno v:
Journal of Enterprise Information Management, 2023, Vol. 37, Issue 1, pp. 24-54.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JEIM-10-2022-0394
