Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

In this report, we introduce the Gemini 1.5 family of models, representing the next generation
of highly compute-efficient multimodal models capable of recalling and reasoning over fine …

We present a method to formulate algorithm discovery as program search, and apply it to
discover optimization algorithms for deep neural network training. We leverage efficient …

Several recent works demonstrate that transformers can implement algorithms like gradient
descent. By a careful construction of weights, these works show that multiple layers of …

Controlling the behavior of language models (LMs) without re-training is a major open
problem in natural language generation. While recent works have demonstrated successes …

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

Realizing quantum speedup for practically relevant, computationally hard problems is a
central challenge in quantum information science. Using Rydberg atom arrays with up to …

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in
the fourth major release of the RELION software. In particular, we introduce VDAM, a …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …