Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …

The prevalent deployment of learning from human preferences through reinforcement
learning (RLHF) relies on two important approximations: the first assumes that pairwise …

A longstanding goal of the field of AI is a method for learning a highly capable, generalist
agent from diverse experience. In the subfields of vision and language, this was largely …

A central goal of machine learning is the development of systems that can solve many
problems in as many data domains as possible. Current architectures, however, cannot be …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

It is common practice in deep learning to represent a measurement of the world on a
discrete grid, eg a 2D grid of pixels. However, the underlying signal represented by these …

We propose a new dataset distillation algorithm using reparameterization and
convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art …

While deep learning models have replaced hand-designed features across many domains,
these models are still trained with hand-designed optimizers. In this work, we leverage the …

A growing body of evidence suggests that neural networks employed in deep reinforcement
learning (RL) gradually lose their plasticity, the ability to learn from new data; however, the …