Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Willmott Devin"'
Vision-language models (VLMs) such as CLIP are trained via contrastive learning between text and image pairs, resulting in aligned image and text embeddings that are useful for many downstream tasks. A notable drawback of CLIP, however, is that the r
Externí odkaz:
http://arxiv.org/abs/2409.09721
Improving RNA secondary structure prediction via state inference with deep recurrent neural networks
Publikováno v:
Computational and Mathematical Biophysics, Vol 8, Iss 1, Pp 36-50 (2020)
The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA
Externí odkaz:
https://doaj.org/article/3b137fc195af411eada1e02ff4cfa82b
Autor:
Wu, Xidong, Lin, Wan-Yi, Willmott, Devin, Condessa, Filipe, Huang, Yufei, Li, Zhenzhen, Ganesh, Madan Ravi
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data. However, FL faces a significant challenge in the form of heterogeneo
Externí odkaz:
http://arxiv.org/abs/2311.08479
Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highly-structured operations like convolutions. Recent ViT-inspired convolutional networks such as C
Externí odkaz:
http://arxiv.org/abs/2210.03651
Autor:
Shmilovich, Kirill, Willmott, Devin, Batalov, Ivan, Kornbluth, Mordechai, Mailoa, Jonathan, Kolter, J. Zico
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fix
Externí odkaz:
http://arxiv.org/abs/2205.06133
Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the "black-box" setting where one only has query access to the classifier.
Externí odkaz:
http://arxiv.org/abs/2102.00029
A community-powered search of machine learning strategy space to find NMR property prediction models
Autor:
Bratholm, Lars A., Gerrard, Will, Anderson, Brandon, Bai, Shaojie, Choi, Sunghwan, Dang, Lam, Hanchar, Pavel, Howard, Addison, Huard, Guillaume, Kim, Sanghoon, Kolter, Zico, Kondor, Risi, Kornbluth, Mordechai, Lee, Youhan, Lee, Youngsoo, Mailoa, Jonathan P., Nguyen, Thanh Tu, Popovic, Milos, Rakocevic, Goran, Reade, Walter, Song, Wonho, Stojanovic, Luka, Thiede, Erik H., Tijanic, Nebojsa, Torrubia, Andres, Willmott, Devin, Butts, Craig P., Glowacki, David R., participants, Kaggle
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in adva
Externí odkaz:
http://arxiv.org/abs/2008.05994
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input. We propose a simple and eff
Externí odkaz:
http://arxiv.org/abs/2007.07210
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scena
Externí odkaz:
http://arxiv.org/abs/1909.13857
Improving RNA secondary structure prediction via state inference with deep recurrent neural networks
Publikováno v:
Computational and Mathematical Biophysics, 8(1), 36-50, 2020
The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA
Externí odkaz:
http://arxiv.org/abs/1906.10819