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pro vyhledávání: '"Ho, Lam"'
Compartmental models, especially the Susceptible-Infected-Removed (SIR) model, have long been used to understand the behaviour of various diseases. Allowing parameters, such as the transmission rate, to be time-dependent functions makes it possible t
Externí odkaz:
http://arxiv.org/abs/2409.17968
Autor:
Wu, Haibin, Chen, Xuanjun, Lin, Yi-Cheng, Chang, Kaiwei, Du, Jiawei, Lu, Ke-Han, Liu, Alexander H., Chung, Ho-Lam, Wu, Yuan-Kuei, Yang, Dongchao, Liu, Songxiang, Wu, Yi-Chiao, Tan, Xu, Glass, James, Watanabe, Shinji, Lee, Hung-yi
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The ideal neural audio codec should maintain content, paralinguistics, spea
Externí odkaz:
http://arxiv.org/abs/2409.14085
Autor:
Rosset, Corby, Chung, Ho-Lam, Qin, Guanghui, Chau, Ethan C., Feng, Zhuo, Awadallah, Ahmed, Neville, Jennifer, Rao, Nikhil
Existing question answering (QA) datasets are no longer challenging to most powerful Large Language Models (LLMs). Traditional QA benchmarks like TriviaQA, NaturalQuestions, ELI5 and HotpotQA mainly study ``known unknowns'' with clear indications of
Externí odkaz:
http://arxiv.org/abs/2402.17896
Autor:
Wu, Haibin, Chen, Xuanjun, Lin, Yi-Cheng, Chang, Kai-wei, Chung, Ho-Lam, Liu, Alexander H., Lee, Hung-yi
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes,
Externí odkaz:
http://arxiv.org/abs/2402.13236
Autor:
Wu, Haibin, Chung, Ho-Lam, Lin, Yi-Cheng, Wu, Yuan-Kuei, Chen, Xuanjun, Pai, Yu-Chi, Wang, Hsiu-Hsuan, Chang, Kai-Wei, Liu, Alexander H., Lee, Hung-yi
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developments in codec models. The ideal sound codec should preserve content, pa
Externí odkaz:
http://arxiv.org/abs/2402.13071
1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in varianc
Externí odkaz:
http://arxiv.org/abs/2312.17480
Autor:
Shih, Min-Han, Chung, Ho-Lam, Pai, Yu-Chi, Hsu, Ming-Hao, Lin, Guan-Ting, Li, Shang-Wen, Lee, Hung-yi
In recent advancements in spoken question answering (QA), end-to-end models have made significant strides. However, previous research has primarily focused on extractive span selection. While this extractive-based approach is effective when answers a
Externí odkaz:
http://arxiv.org/abs/2312.09781
Autor:
Nguyen, Cuong N., Tran, Phong, Ho, Lam Si Tung, Dinh, Vu, Tran, Anh T., Hassner, Tal, Nguyen, Cuong V.
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationall
Externí odkaz:
http://arxiv.org/abs/2312.00656
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
Autor:
Shi, Jiatong, Chen, William, Berrebbi, Dan, Wang, Hsiu-Hsuan, Huang, Wei-Ping, Hu, En-Pei, Chuang, Ho-Lam, Chang, Xuankai, Tang, Yuxun, Li, Shang-Wen, Mohamed, Abdelrahman, Lee, Hung-yi, Watanabe, Shinji
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises
Externí odkaz:
http://arxiv.org/abs/2310.05513
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