no code implementations • 16 May 2024 • George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann, Yi Kan Wang, Jeremy D. Kunz, Juan A. Retamero, Gerard Oakley, David Klimstra, Christopher Kanan, Matthew Hanna, Michal Zelechowski, Julian Viret, Neil Tenenholtz, James Hall, Nicolo Fusi, Razik Yousfi, Peter Hamilton, William A. Moye, Eugene Vorontsov, SiQi Liu, Thomas J. Fuchs
Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine.
1 code implementation • 6 Feb 2024 • Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolo Fusi
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language.
1 code implementation • 28 Nov 2023 • Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #2 on Question Answering on MedQA
no code implementations • 14 Sep 2023 • Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, SiQi Liu, Kristen Severson, Eric Zimmermann, James Hall, Neil Tenenholtz, Nicolo Fusi, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew Hanna, Juan Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David Klimstra, Brandon Rothrock, Thomas J. Fuchs
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
no code implementations • 4 Aug 2022 • Neha Hulkund, Nicolo Fusi, Jennifer Wortman Vaughan, David Alvarez-Melis
We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport.
no code implementations • 25 Jun 2022 • Syrine Belakaria, Janardhan Rao Doppa, Nicolo Fusi, Rishit Sheth
The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training.
no code implementations • 21 Oct 2021 • Samyadeep Basu, Amr Sharaf, Nicolo Fusi, Soheil Feizi
To address the issue of sub-par performance on hard episodes, we investigate and benchmark different meta-training strategies based on adversarial training and curriculum learning.
no code implementations • 29 Sep 2021 • Pavlo Molchanov, Jimmy Hall, Hongxu Yin, Jan Kautz, Nicolo Fusi, Arash Vahdat
In the second phase, it solves the combinatorial selection of efficient operations using a novel constrained integer linear optimization approach.
no code implementations • 10 Sep 2021 • Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolo Fusi, Robert Mullins
H-Meta-NAS shows a Pareto dominance compared to a variety of NAS and manual baselines in popular few-shot learning benchmarks with various hardware platforms and constraints.
no code implementations • 12 Jul 2021 • Pavlo Molchanov, Jimmy Hall, Hongxu Yin, Jan Kautz, Nicolo Fusi, Arash Vahdat
We analyze three popular network architectures: EfficientNetV1, EfficientNetV2 and ResNeST, and achieve accuracy improvement for all models (up to $3. 0\%$) when compressing larger models to the latency level of smaller models.
no code implementations • 20 Oct 2020 • Anant Raj, Cameron Musco, Lester Mackey, Nicolo Fusi
Model selection requires repeatedly evaluating models on a given dataset and measuring their relative performances.
no code implementations • 20 Mar 2020 • Diana Cai, Rishit Sheth, Lester Mackey, Nicolo Fusi
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples.
no code implementations • 27 Aug 2019 • Rishit Sheth, Nicolo Fusi
In this paper we introduce Feature Gradients, a gradient-based search algorithm for feature selection.
no code implementations • ICLR 2019 • Ruishan Liu, Nicolo Fusi, Lester Mackey
Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as deep neural networks and large random forests on both image and tabular datasets.
no code implementations • 13 Feb 2019 • Francesco Paolo Casale, Jonathan Gordon, Nicolo Fusi
We showcase the advantages of our approach in applications to CIFAR-10 and ImageNet, where our approach outperforms methods with double its computational cost and matches the performance of methods with costs that are three orders of magnitude larger.
1 code implementation • ICLR 2019 • Ruishan Liu, Nicolo Fusi, Lester Mackey
Our GAN-assisted TSC (GAN-TSC) significantly improves student accuracy for expensive models such as large random forests and deep neural networks on both tabular and image datasets.
2 code implementations • NeurIPS 2018 • Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue.
1 code implementation • NeurIPS 2018 • Nicolo Fusi, Rishit Sheth, Huseyn Melih Elibol
Automating the selection and tuning of machine learning pipelines consisting of data pre-processing methods and machine learning models, has long been one of the goals of the machine learning community.
8 code implementations • 26 Sep 2013 • James Hensman, Nicolo Fusi, Neil D. Lawrence
We introduce stochastic variational inference for Gaussian process models.