2 links tagged with all of: machine-learning + generative-modeling
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This article discusses BaNEL, a new algorithm that improves generative models by training them using only negative reward samples. It addresses the challenges of reward sparsity and costly evaluations in complex problem-solving scenarios, demonstrating its effectiveness through various experiments.
CrystalFormer is a transformer-based autoregressive model tailored for generating crystalline materials while adhering to space group symmetry, enhancing data and computational efficiency. It allows for conditional generation through a structured framework, which includes reinforcement learning and Markov chain Monte Carlo methods. The model supports various functionalities such as generating specific crystal structures and evaluating their validity and novelty.