2 code implementations • 13 Apr 2023 • Guillaume Jaume, Anurag Vaidya, Richard Chen, Drew Williamson, Paul Liang, Faisal Mahmood
We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.
no code implementations • 29 Mar 2023 • El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
1 code implementation • NeurIPS 2023 • Paul Pu Liang, Yun Cheng, Xiang Fan, Chun Kai Ling, Suzanne Nie, Richard Chen, Zihao Deng, Nicholas Allen, Randy Auerbach, Faisal Mahmood, Ruslan Salakhutdinov, Louis-Philippe Morency
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
no code implementations • 18 Jan 2021 • Dana Jones, Skyler Palatnick, Richard Chen, Angus Beane, Adam Lidz
If FDM constitutes the entirety of the dark matter and the FDM particle mass is 10-21eV, HERA can determine the mass to within 20 percent at 2-sigma confidence.
Cosmology and Nongalactic Astrophysics Computational Physics
no code implementations • 7 Aug 2020 • Anitha Kannan, Richard Chen, Vignesh Venkataraman, Geoffrey J. Tso, Xavier Amatriain
Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today.
no code implementations • 21 Oct 2019 • Arthur G. Rattew, Shaohan Hu, Marco Pistoia, Richard Chen, Steve Wood
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest.
no code implementations • 8 Dec 2018 • Tim Salimans, Richard Chen
We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge.
1 code implementation • 29 Sep 2018 • Faisal Mahmood, Daniel Borders, Richard Chen, Gregory N. McKay, Kevan J. Salimian, Alexander Baras, Nicholas J. Durr
However, CNNs require large amounts of labeled histopathology data.
no code implementations • 22 Aug 2018 • Richard Chen, Faisal Mahmood, Alan Yuille, Nicholas J. Durr
Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function.
no code implementations • 22 May 2018 • Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J. Durr
Our experiments demonstrate that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model.
no code implementations • 17 Nov 2017 • Faisal Mahmood, Richard Chen, Nicholas J. Durr
We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization.
no code implementations • 4 Aug 2016 • Richard Chen, Yating Jing, Hunter Jackson
Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer.