Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence.
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.
Ranked #1 on Text-to-Image Generation on COCO
The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution.
Ranked #4 on 6D Pose Estimation using RGB on LineMOD
Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Ranked #12 on Text-to-Image Generation on COCO (using extra training data)
In our model, medical text annotation is introduced to compensate for the quality deficiency in image data.