Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with
access to a machine learning model from extracting information about individual training points…

Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences,
but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated …

The top-k error is a common measure of performance in machine learning and computer
vision. In practice, top-k classification is typically performed with deep neural networks trained …

The ability to generate privacy-preserving synthetic versions of sensitive image datasets
could unlock numerous ML applications currently constrained by data availability. Due to their …

Many researchers believe that ConvNets perform well on small or moderately sized datasets,
but are not competitive with Vision Transformers when given access to datasets on the web…

In modern supervised learning, many deep neural networks are able to interpolate the data:
the empirical loss can be driven to near zero on all samples simultaneously. In this work, we …

We introduce RecurrentGemma, a family of open language models which uses Google's
novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve …

Advanced AI assistants combine frontier LLMs and tool access to autonomously perform
complex tasks on behalf of users. While the helpfulness of such assistants can increase …

Learning a deep neural network requires solving a challenging optimization problem: it is a
high-dimensional, non-convex and non-smooth minimization problem with a large number of …

Privacy-preserving machine learning aims to train models on private data without leaking
sensitive information. Differential privacy (DP) is considered the gold standard framework for …