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Found 7 papers, 1 papers with code
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks
In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.
Does Adversarial Oversampling Help us?
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
Latent Preserving Generative Adversarial Network for Imbalance classification
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
Robust Representation Learning with Self-Distillation for Domain Generalization
Despite the recent success of deep neural networks, there remains a need for effective methods to enhance domain generalization using vision transformers.
S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning
This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks.
Cross-Problem Learning for Solving Vehicle Routing Problems
Accordingly, we propose to pre-train the backbone Transformer for TSP, and then apply it in the process of fine-tuning the Transformer models for each target VRP variant.
