Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Goh, Hanlin"'
Sleep staging is a clinically important task for diagnosing various sleep disorders, but remains challenging to deploy at scale because it because it is both labor-intensive and time-consuming. Supervised deep learning-based approaches can automate s
Externí odkaz:
http://arxiv.org/abs/2404.15308
Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks. However, such models mainly perform zero-shot recognition in a closed-set manner, and thus struggle to handle open-domain visual concepts by desig
Externí odkaz:
http://arxiv.org/abs/2401.15914
Autor:
Thilak, Vimal, Huang, Chen, Saremi, Omid, Dinh, Laurent, Goh, Hanlin, Nakkiran, Preetum, Susskind, Joshua M., Littwin, Etai
Joint embedding (JE) architectures have emerged as a promising avenue for acquiring transferable data representations. A key obstacle to using JE methods, however, is the inherent challenge of evaluating learned representations without access to a do
Externí odkaz:
http://arxiv.org/abs/2312.04000
Autor:
Liu, Ran, Zippi, Ellen L., Pouransari, Hadi, Sandino, Chris, Nie, Jingping, Goh, Hanlin, Azemi, Erdrin, Moin, Ali
Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often exhibit substantial distributional shifts between pretraining and infe
Externí odkaz:
http://arxiv.org/abs/2309.05927
Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require dif
Externí odkaz:
http://arxiv.org/abs/2303.03679
Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for E
Externí odkaz:
http://arxiv.org/abs/2211.02625
The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc. Nonetheless, this direction has not been well-explored
Externí odkaz:
http://arxiv.org/abs/2209.13156
Autor:
Bautista, Miguel Angel, Guo, Pengsheng, Abnar, Samira, Talbott, Walter, Toshev, Alexander, Chen, Zhuoyuan, Dinh, Laurent, Zhai, Shuangfei, Goh, Hanlin, Ulbricht, Daniel, Dehghan, Afshin, Susskind, Josh
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we
Externí odkaz:
http://arxiv.org/abs/2207.13751
Autor:
Zhai, Shuangfei, Jaitly, Navdeep, Ramapuram, Jason, Busbridge, Dan, Likhomanenko, Tatiana, Cheng, Joseph Yitan, Talbott, Walter, Huang, Chen, Goh, Hanlin, Susskind, Joshua
Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of their powerful representational capacity. However, harnessing this represe
Externí odkaz:
http://arxiv.org/abs/2207.07611
Autor:
Littwin, Etai, Saremi, Omid, Zhai, Shuangfei, Thilak, Vimal, Goh, Hanlin, Susskind, Joshua M., Yang, Greg
We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck. Unlike the neural tangent kernel limit, a bottleneck in an otherwise infinite width network al-lows data dependent feature learning in its bottle-nec
Externí odkaz:
http://arxiv.org/abs/2107.00364