Technology
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
We explore the methodology and theory of reward-directed generation via conditional diffusion models.
1 Supplementary
Code and data to replicate our experiments can be found at https://github.com/ppope/rho-learn. 1.1 DFT Relaxations We use the PBE exchange-correlation functional for all relaxations. In particular a much smaller model was used than the state-of-the-art SCN results. An SCF run may be initialized with a custom density, e.g. one generated from a machine-learning
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts.
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of research in cognitive science has hypothesized a process, "iterated learning,"
Phased Consistency Models Fu-Y un Wang
Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory.
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
However, zero-shot learning methods still require human instruction set to solve a new task.