Google & JHU Paper Explores and Categorizes Neural Scaling Laws
A research team from Google and Johns Hopkins University identifies variance-limited and resolution-limited scaling behaviours for dataset and model size in four scaling regimes.
Tag: Johns Hopkins University
Google & Johns Hopkins University | Can Adversarial Examples Improve Image Recognition?
Rather than attempting to defend convolutional networks against them, the researchers introduce a novel enhanced adversarial training scheme, AdvProp, which treats adversarial examples as additional training examples to improve the accuracy of image classification models.
Insilico Medicine Announces MOSES Benchmark Platform for Molecular Generation
Insilico Medicine, a drug discovery startup located in Johns Hopkins University, has introduced MOSES (Molecular Sets), a platform which can be used to compare model accuracy in molecular generation. MOSES provides a standardized benchmarking dataset, a set of open-sourced models with unified implementation, and evaluation metrics.