Harvard University

9 Posts

Deep Learning Discovers Antibiotics: Researchers used neural networks to find a new class of antibiotics.
Harvard University

Deep Learning Discovers Antibiotics: Researchers used neural networks to find a new class of antibiotics.

Biologists used neural networks to find a new class of antibiotics. Researchers at MIT and Harvard trained models to screen chemical compounds for those that kill methicillin-resistant Staphylococcus aureus (MRSA), the deadliest among bacteria that have...
A new framework that helps models “unlearn” information selectively and incrementally
Harvard University

Deep Unlearning: AI Researchers Teach Models to Unlearn Data

Privacy advocates want deep learning systems to forget what they’ve learned. What’s new: Researchers are seeking ways to remove the influence of particular training examples, such as an individual’s personal information, from a trained model without affecting its performance, Wired reported.
Data related to a system that purportedly identified breast cancer
Harvard University

Pushing for Reproducible Research: Experts criticize Google Health over AI transparency.

Controversy erupted over the need for transparency in research into AI for medicine. Google Health introduced a system that purportedly identified breast cancer more accurately than human radiologists.
Images containing various atlas and graphs
Harvard University

Underwater Atlas: Deep learning helps scientists map undersea ecosystems.

The ocean contains distinct ecosystems, but they’re much harder to see than terrestrial forests or savannas. A new model helps scientists better understand patterns of undersea life, which is threatened by pollution, invasive species, and warming temperatures.
Graphs related to double descent
Harvard University

Moderating the ML Roller Coaster: A technique to avoid double descent in AI

Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.
Illustration of a patient in a hospital bed
Harvard University

Prognosis — Early Warning for Sepsis: AI can provide an early warning for sepsis.

An AI-driven alarm system helps rescue patients before infections become fatal. The problem: Machine learning can spot patterns in electronic health data indicating where a patient’s condition is headed that may be too subtle for doctors and nurses to catch.
Data related to model that predicts molecules that are structurally unrelated to known antibiotics
Harvard University

Deep Learning Finds New Antibiotic: Researchers used AI to identify a promising new antibiotic.

Chemists typically develop new antibiotics by testing close chemical relatives of tried-and-true compounds like penicillin. That approach becomes less effective, though, as dangerous bacteria evolve resistance to those very chemical structures. Instead, researchers enlisted neural networks.
Graphs related to ImageNet error landscape
Harvard University

Rightsizing Neural Nets: An equation for predicting optimal data and model size

How much data do we want? More! How large should the model be? Bigger! How much more and how much bigger? New research estimates the impact of dataset and model sizes on neural network performance.
Graph related to LIME and SHAP methods
Harvard University

Bias Goes Undercover: Adversarial attacks can fool explainable AI techniques.

As black-box algorithms like neural networks find their way into high-stakes fields such as transportation, healthcare, and finance, researchers have developed techniques to help explain models’ decisions. New findings show that some of these methods can be fooled.

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