Search Results for author: Li Yao

Found 18 papers, 8 papers with code

IBEX: An open and extensible method for high content multiplex imaging of diverse tissues

no code implementations23 Jul 2021 Andrea J. Radtke, Colin J. Chu, Ziv Yaniv, Li Yao, James Marr, Rebecca T. Beuschel, Hiroshi Ichise, Anita Gola, Juraj Kabat, Bradley Lowekamp, Emily Speranza, Joshua Croteau, Nishant Thakur, Danny Jonigk, Jeremy Davis, Jonathan M. Hernandez, Ronald N. Germain

We recently developed Iterative Bleaching Extends multi-pleXity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts.

On the diminishing return of labeling clinical reports

no code implementations EMNLP (ClinicalNLP) 2020 Jean-Baptiste Lamare, Tobi Olatunji, Li Yao

Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains.

Learning to estimate label uncertainty for automatic radiology report parsing

no code implementations1 Oct 2019 Tobi Olatunji, Li Yao

Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging.

Caveats in Generating Medical Imaging Labels from Radiology Reports

1 code implementation6 May 2019 Tobi Olatunji, Li Yao, Ben Covington, Alexander Rhodes, Anthony Upton

Acquiring high-quality annotations in medical imaging is usually a costly process.

A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging

no code implementations2 Apr 2019 Li Yao, Jordan Prosky, Ben Covington, Kevin Lyman

This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging.

Domain Adaptation Multi-target Domain Adaptation

Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

no code implementations21 Mar 2018 Li Yao, Jordan Prosky, Eric Poblenz, Ben Covington, Kevin Lyman

Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance.

Medical Diagnosis

Learning to diagnose from scratch by exploiting dependencies among labels

10 code implementations ICLR 2018 Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman

The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.

14 Multi-Label Classification +1

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Delving Deeper into Convolutional Networks for Learning Video Representations

2 code implementations19 Nov 2015 Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville

We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs). Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset.

Action Recognition Video Captioning

Oracle performance for visual captioning

1 code implementation14 Nov 2015 Li Yao, Nicolas Ballas, Kyunghyun Cho, John R. Smith, Yoshua Bengio

The task of associating images and videos with a natural language description has attracted a great amount of attention recently.

Image Captioning Language Modelling +1

GSNs : Generative Stochastic Networks

no code implementations18 Mar 2015 Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood.


Describing Videos by Exploiting Temporal Structure

5 code implementations ICCV 2015 Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville

In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.

Action Recognition Video Description

On the Equivalence Between Deep NADE and Generative Stochastic Networks

no code implementations2 Sep 2014 Li Yao, Sherjil Ozair, Kyunghyun Cho, Yoshua Bengio

Orderless NADEs are trained based on a criterion that stochastically maximizes $P(\mathbf{x})$ with all possible orders of factorizations.

Iterative Neural Autoregressive Distribution Estimator (NADE-k)

1 code implementation5 Jun 2014 Tapani Raiko, Li Yao, Kyunghyun Cho, Yoshua Bengio

Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data.

Density Estimation Image Generation +1

Multimodal Transitions for Generative Stochastic Networks

no code implementations19 Dec 2013 Sherjil Ozair, Li Yao, Yoshua Bengio

Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distribution.

Bounding the Test Log-Likelihood of Generative Models

no code implementations24 Nov 2013 Yoshua Bengio, Li Yao, Kyunghyun Cho

Several interesting generative learning algorithms involve a complex probability distribution over many random variables, involving intractable normalization constants or latent variable normalization.

Generalized Denoising Auto-Encoders as Generative Models

1 code implementation NeurIPS 2013 Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued.


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