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Found 11 papers, 3 papers with code
Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation
Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems.
Continual learning with task specialist
In this paper, we propose Continual Learning with Task Specialists (CLTS) to address the issues of catastrophic forgetting and limited labelled data in real-world datasets by performing class incremental learning of the incoming stream of data.
XMOL: Explainable Multi-property Optimization of Molecules
Molecular optimization is a key challenge in drug discovery and material science domain, involving the design of molecules with desired properties.
U-TELL: Unsupervised Task Expert Lifelong Learning
To address these issues, we propose an unsupervised CL model with task experts called Unsupervised Task Expert Lifelong Learning (U-TELL) to continually learn the data arriving in a sequence addressing catastrophic forgetting.
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.
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.
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.
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.
Does Adversarial Oversampling Help us?
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
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.
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.
