Fudan University

Researchers devised a way to reduce memory requirements when fine-tuning large language models. Kai Lv and colleagues at Fudan University proposed low memory optimization (LOMO), a modification of stochastic gradient descent that stores less data than other optimizers during fine-tuning.

Two years after it pointed a new direction for language models, Bert still hovers near the top of several natural language processing leaderboards. A new study considers whether Bert simply excels at tracking word order or or learns something closer to common sense.

Hoping to keep surveillance capitalists from capitalizing on your face? Safeguard your selfies with a digital countermeasure. Researchers devised a program that subtly alters portrait photos to confuse face recognition models without distorting the image to the human eye.