The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …

There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …

Inference from large autoregressive models like Transformers is slow-decoding K tokens
takes K serial runs of the model. In this work we introduce speculative decoding-an …

As edge devices equipped with cameras and inertial measurement units (IMUs) are
emerging, it holds huge implications to endow these mobile devices with spatial computing …

Transformer models have been widely adopted in various domains over the last years and
especially large language models have advanced the field of AI significantly. Due to their …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Language models (LMs) are powerful tools for natural language processing, but they often
struggle to produce coherent and fluent text when they are small. Models with around 125M …

Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …

Federated Learning (FL) is a machine learning setting where many devices collaboratively
train a machine learning model while keeping the training data decentralized. In most of the …