Machine learning (ML) has rapidly become one of the most influential technologies across industries, from healthcare and ...

The process begins with feeding an algorithm enormous amounts of data—books, math problems, captioned photos, voice recordings, and so on—to establish the model’s baseline capabilities.

Research has shown that parameters pruned after training, a process that decreases the model size, could have been pruned before training without any effect on the network’s ability to learn.

Google announced the release of the Quantization Aware Training (QAT) API for their TensorFlow Model Optimization Toolkit. QAT simulates low-precision hardware during the neural-network training ...

Underspecification means something different: even if a training process can produce a good model, it could still spit out a bad one because it won’t know the difference. Neither would we.

"Model collapse is a degenerative process affecting generations of learned generative models, in which the data they generate end up polluting the training set of the next generation," Shumailov's ...