MIT

62 Posts

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

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...

January 10, 20242 min read
Deep Learning Discovers Antibiotics: Researchers used neural networks to find a new class of antibiotics.
MIT

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...

January 10, 20242 min read
Synthetic Data Helps Image Classification: StableRep, a method that trains vision transformers on images generated by Stable Diffusion
MIT

Synthetic Data Helps Image Classification: StableRep, a method that trains vision transformers on images generated by Stable Diffusion

Generated images can be more effective than real ones in training a vision model to classify images. Yonglong Tian, Lijie Fan, and colleagues at Google and MIT introduced StableRep, a self-supervised method that trains vision transformers on images generated by...

November 8, 20232 min read
Synthetic Data Helps Image Classification: StableRep, a method that trains vision transformers on images generated by Stable Diffusion
MIT

Synthetic Data Helps Image Classification: StableRep, a method that trains vision transformers on images generated by Stable Diffusion

Generated images can be more effective than real ones in training a vision model to classify images. Yonglong Tian, Lijie Fan, and colleagues at Google and MIT introduced StableRep, a self-supervised method that trains vision transformers on images generated by...

November 8, 20232 min read
Segmented Images, No Labeled Data: Improved unsupervised learning for semantic segmentation
MIT

Segmented Images, No Labeled Data: Improved unsupervised learning for semantic segmentation

Training a model to separate the objects in a picture typically requires labeled images for best results. Recent work upped the ante for training without labels.

January 11, 20232 min read
Segmented Images, No Labeled Data: Improved unsupervised learning for semantic segmentation
MIT

Segmented Images, No Labeled Data: Improved unsupervised learning for semantic segmentation

Training a model to separate the objects in a picture typically requires labeled images for best results. Recent work upped the ante for training without labels.

January 11, 20232 min read
AI as Officemate: Workers benefit from AI-powered assistance and tools.
MIT

AI as Officemate: Workers benefit from AI-powered assistance and tools.

Many workers benefit from AI in the office without knowing it, a new study found. MIT Sloan Management Review and Boston Consulting Group surveyed employees on their use of AI in their day-to-day work. Their findings...

January 4, 20232 min read
AI as Officemate: Workers benefit from AI-powered assistance and tools.
MIT

AI as Officemate: Workers benefit from AI-powered assistance and tools.

Many workers benefit from AI in the office without knowing it, a new study found. MIT Sloan Management Review and Boston Consulting Group surveyed employees on their use of AI in their day-to-day work. Their findings...

January 4, 20232 min read
Champion Model Is No Go: Adversarial AI Beats Master KataGo Algorithm
MIT

Champion Model Is No Go: Adversarial AI Beats Master KataGo Algorithm

A new algorithm defeated a championship-winning Go model using moves that even a middling human player could counter. Researchers trained a model to defeat KataGo, an open source Go-playing system that has beaten top human players.

November 23, 20222 min read
Champion Model Is No Go: Adversarial AI Beats Master KataGo Algorithm
MIT

Champion Model Is No Go: Adversarial AI Beats Master KataGo Algorithm

A new algorithm defeated a championship-winning Go model using moves that even a middling human player could counter. Researchers trained a model to defeat KataGo, an open source Go-playing system that has beaten top human players.

November 23, 20222 min read
AI Sees Race in X-Rays
MIT

AI Sees Race in X-Rays

Researchers from Emory University, MIT, Purdue University, and other institutions found that deep learning systems trained to interpret x-rays and CT scans also were able to identify their subjects as Asian, Black, or White.

August 11, 20212 min read
AI Sees Race in X-Rays
MIT

AI Sees Race in X-Rays

Researchers from Emory University, MIT, Purdue University, and other institutions found that deep learning systems trained to interpret x-rays and CT scans also were able to identify their subjects as Asian, Black, or White.

August 11, 20212 min read
3D Scene Synthesis for the Real World: Generating 3D scenes with radiance fields and image data
MIT

3D Scene Synthesis for the Real World: Generating 3D scenes with radiance fields and image data

Researchers have used neural networks to generate novel views of a 3D scene based on existing pictures plus the positions and angles of the cameras that took them. In practice, though, you may not know the precise camera

June 9, 20212 min read
3D Scene Synthesis for the Real World: Generating 3D scenes with radiance fields and image data
MIT

3D Scene Synthesis for the Real World: Generating 3D scenes with radiance fields and image data

Researchers have used neural networks to generate novel views of a 3D scene based on existing pictures plus the positions and angles of the cameras that took them. In practice, though, you may not know the precise camera

June 9, 20212 min read
Labeling Errors Everywhere: Many deep learning datasets contain mislabeled data.
MIT

Labeling Errors Everywhere: Many deep learning datasets contain mislabeled data.

Key machine learning datasets are riddled with mistakes. Several benchmark datasets are shot through with incorrect labels. On average, 3.4 percent of examples in 10 commonly used datasets are mislabeled and the detrimental impact of such errors rises with model size.

April 14, 20212 min read

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