no code implementations • 24 Oct 2024 • Sicong Huang, Roozbeh Jafari, Bobak J. Mortazavi
We validated ArterialNet using the MIMIC-III dataset and achieved a root mean square error (RMSE) of 5. 41 mmHg, with at least a 58% lower standard deviation.
1 code implementation • 10 Jan 2024 • Sicong Huang, JiaWei He, Kry Yik Chau Lui
Second, introducing new theoretic tools such as nearly essential support, essential distance and co-Lipschitzness, we obtain non-asymptotic provable OOD detection guarantees for certain distillation of the minimal sufficient statistics.
no code implementations • 15 Jun 2023 • Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
1 code implementation • ICLR 2022 • Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Grosse, Alireza Makhzani
Since accurate estimation of MI without density information requires a sample size exponential in the true MI, we assume either a single marginal or the full joint density information is known.
no code implementations • 7 Feb 2023 • Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Grosse
It is often useful to compactly summarize important properties of model parameters and training data so that they can be used later without storing and/or iterating over the entire dataset.
no code implementations • 15 Nov 2022 • Sicong Huang, Asli Celikyilmaz, Haoran Li
Abstractive summarization models typically generate content unfaithful to the input, thus highlighting the significance of evaluating the faithfulness of generated summaries.
2 code implementations • ICML 2020 • Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge.
1 code implementation • WS 2019 • Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, Fei Xia
Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.
1 code implementation • ICLR 2019 • Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.
1 code implementation • ICLR 2018 • Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser
This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.