Generative AI in the era of 'alternative facts'
|
MIT Open Publishing Services
Article
Toward robust mammography-based models for breast cancer risk
Details
author(s)
Regina Barzilay
Adam Yala
publication date
26 January 2021
source
Science
related programme
MIT Jameel Clinic
Link to publication
External link ->
External data and AI are making each other more valuable
|
Harvard Business Review Press
Rethinking patch dependence for masked autoencoders
|
Arxiv
Removing biases from molecular representations via information maximisation
|
Arxiv
Effective human-AI teams via learned natural language rules and onboarding
|
Arxiv
A deep dive into single-cell RNA sequencing foundation models
|
bioRxiv
Antibiotic identified by AI
|
Nature
LLM-grounded video diffusion models
|
Arxiv
Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features
|
Pancreas
Leveraging artificial intelligence in the fight against infectious diseases
|
Science
BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences
|
Cell Systems
Conformal language modeling
|
Arxiv
Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction: An observational study
|
Radiological Society of North America
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
|
Nature
Algorithmic pluralism: A structural approach towards equal opportunity
|
Arxiv
Artificial intelligence and machine learning in lung cancer screening
|
Science Direct
Wide and deep neural networks achieve consistency for classification
|
PNAS
Autocatalytic base editing for RNA-responsive translational control
|
Nature
DiffDock: Diffusion steps, twists and turns for molecular docking
|
Arxiv
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography
|
Journal of Clinical Oncology
Sequential multi-dimensional self-supervised learning for clinical time series
|
Proceedings of Machine Learning Research
Queueing theory: Classical and modern methods
|
Dynamic Ideas
Upper body thermal images and associated clinical data from a pilot cohort study of COVID-19
|
Physionet
Toward robust mammography-based models for breast cancer risk
|
Science
The age of AI: And our human future
|
Little, Brown and Company
Uniform priors for data-efficient transfer
|
Arxiv
Machine learning under a modern optimisation lens
|
Dynamic Ideas
The marginal value of adaptive gradient methods in machine learning
|
Advances in Neural Information Processing Systems
Efficient graph-based image segmentation
|
International Journal of Computer Vision