Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

By encoding time series as a string of numerical digits, we can frame time series forecasting
as next-token prediction in text. Developing this approach, we find that large language …

We propose a simple pairwise sigmoid loss for image-text pre-training. Unlike standard
contrastive learning with softmax normalization, the sigmoid loss operates solely on image …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are
rapidly transforming the way we communicate, illustrate, and create. However, AI regulation …

Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …