Zobrazeno 1 - 10
of 548
pro vyhledávání: '"Nguyen-Binh T"'
Autor:
Le, Lien P., Thi, Xuan-Hien Nguyen, Nguyen, Thu, Riegler, Michael A., Halvorsen, Pål, Nguyen, Binh T.
Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to d
Externí odkaz:
http://arxiv.org/abs/2412.11164
Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global model to perform well on a sequence of tasks without sharing their priva
Externí odkaz:
http://arxiv.org/abs/2407.11078
Autor:
Le, Hoang H., Nguyen, Duy M. H., Bhatti, Omair Shahzad, Kopacsi, Laszlo, Ngo, Thinh P., Nguyen, Binh T., Barz, Michael, Sonntag, Daniel
Comprehending how humans process visual information in dynamic settings is crucial for psychology and designing user-centered interactions. While mobile eye-tracking systems combining egocentric video and gaze signals can offer valuable insights, man
Externí odkaz:
http://arxiv.org/abs/2406.06239
Autor:
Tran, Hoai-Chau, Nguyen, Duy M. H., Nguyen, Duy M., Nguyen, Trung-Tin, Le, Ngan, Xie, Pengtao, Sonntag, Daniel, Zou, James Y., Nguyen, Binh T., Niepert, Mathias
Increasing the throughput of the Transformer architecture, a foundational component used in numerous state-of-the-art models for vision and language tasks (e.g., GPT, LLaVa), is an important problem in machine learning. One recent and effective strat
Externí odkaz:
http://arxiv.org/abs/2405.16148
Autor:
Pham, Quang, Do, Giang, Nguyen, Huy, Nguyen, TrungTin, Liu, Chenghao, Sartipi, Mina, Nguyen, Binh T., Ramasamy, Savitha, Li, Xiaoli, Hoi, Steven, Ho, Nhat
Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, effective training of SMoE has proven to be challenging due to the representation coll
Externí odkaz:
http://arxiv.org/abs/2402.02526
The Gromov-Wasserstein (GW) distance is an extension of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is specified as the solution of a non-convex quadratic program and is not
Externí odkaz:
http://arxiv.org/abs/2312.14572
Autor:
Nguyen, Duy Minh Ho, Pham, Tan Ngoc, Diep, Nghiem Tuong, Phan, Nghi Quoc, Pham, Quang, Tong, Vinh, Nguyen, Binh T., Le, Ngan Hoang, Ho, Nhat, Xie, Pengtao, Sonntag, Daniel, Niepert, Mathias
Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of n
Externí odkaz:
http://arxiv.org/abs/2311.11096
Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock
Externí odkaz:
http://arxiv.org/abs/2310.05892
Autor:
Nguyen, Duy M. H., Nguyen, Hoang, Diep, Nghiem T., Pham, Tan N., Cao, Tri, Nguyen, Binh T., Swoboda, Paul, Ho, Nhat, Albarqouni, Shadi, Xie, Pengtao, Sonntag, Daniel, Niepert, Mathias
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained o
Externí odkaz:
http://arxiv.org/abs/2306.11925
Autor:
Pham, Nhat-Hao, Vo, Khanh-Linh, Vu, Mai Anh, Nguyen, Thu, Riegler, Michael A., Halvorsen, Pål, Nguyen, Binh T.
Correlation matrix visualization is essential for understanding the relationships between variables in a dataset, but missing data can pose a significant challenge in estimating correlation coefficients. In this paper, we compare the effects of vario
Externí odkaz:
http://arxiv.org/abs/2305.06044