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pro vyhledávání: '"Yip P"'
With the James Webb Space Telescope (JWST) offering higher resolution data in space-based transmission spectroscopy, understanding the capabilities of our current atmospheric retrieval pipelines is essential. These new data cover wider wavelength ran
Externí odkaz:
http://arxiv.org/abs/2407.09296
This work introduces an approach to enhancing the computational efficiency of 3D atmospheric simulations by integrating a machine-learned surrogate model into the OASIS global circulation model (GCM). Traditional GCMs, which are based on repeatedly n
Externí odkaz:
http://arxiv.org/abs/2407.08556
Continual Learning (CL) involves fine-tuning pre-trained models with new data while maintaining the performance on the pre-trained data. This is particularly relevant for expanding multilingual ASR (MASR) capabilities. However, existing CL methods, m
Externí odkaz:
http://arxiv.org/abs/2407.03645
Autor:
Greaves, Gary, Yip, Jose
We characterise graphs that have three distinct eigenvalues and coherent ranks 8 and 9, linking the former to certain symmetric 2-designs and the latter to specific quasi-symmetric 2-designs. This characterisation leads to the discovery of a new bire
Externí odkaz:
http://arxiv.org/abs/2406.17395
Cohort studies are of significant importance in the field of healthcare analysis. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical
Externí odkaz:
http://arxiv.org/abs/2406.14015
Recent improvements in neural audio codec (NAC) models have generated interest in adopting pre-trained codecs for a variety of speech processing applications to take advantage of the efficiencies gained from high compression, but these have yet been
Externí odkaz:
http://arxiv.org/abs/2406.12434
Autor:
Gross, Jason, Agrawal, Rajashree, Kwa, Thomas, Ong, Euan, Yip, Chun Hei, Gibson, Alex, Noubir, Soufiane, Chan, Lawrence
We propose using mechanistic interpretability -- techniques for reverse engineering model weights into human-interpretable algorithms -- to derive and compactly prove formal guarantees on model performance. We prototype this approach by formally prov
Externí odkaz:
http://arxiv.org/abs/2406.11779
Autor:
Yip, Hiu Ching, Valente, Daria, Bibbona, Enrico, Friard, Olivier, Mastrantonio, Gianluca, Gamba, Marco
This paper proposes a hierarchical spatial-temporal model for modelling the spectrograms of animal calls. The motivation stems from analyzing recordings of the so-called grunt calls emitted by various lemur species. Our goal is to identify a latent s
Externí odkaz:
http://arxiv.org/abs/2406.04915
Autor:
Zhou, Kun, Zhao, Shengkui, Ma, Yukun, Zhang, Chong, Wang, Hao, Ng, Dianwen, Ni, Chongjia, Hieu, Nguyen Trung, Yip, Jia Qi, Ma, Bin
Recent language model-based text-to-speech (TTS) frameworks demonstrate scalability and in-context learning capabilities. However, they suffer from robustness issues due to the accumulation of errors in speech unit predictions during autoregressive l
Externí odkaz:
http://arxiv.org/abs/2406.02009
Autor:
Yip, Chi Hoi
Publikováno v:
Bull. Aust. Math. Soc., 2024
Let $n$ be a non-zero integer. A set $S$ of positive integers is a Diophantine tuple with the property $D(n)$ if $ab+n$ is a perfect square for each $a,b \in S$ with $a \neq b$. It is of special interest to estimate the quantity $M_n$, the maximum si
Externí odkaz:
http://arxiv.org/abs/2406.00840