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Found 8 papers, 5 papers with code
Graphs Generalization under Distribution Shifts
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution.
Sophisticated Behavioral Simulation: A Possible Solution to Problems of Organized Complexity
As a matter of facts, they act as the best tool to handle problems in complex systems where closed-form expressions are unavailable and the target distribution in the representation space is too complex to be fully represented by data-driven learning models, such as deep learning (DL) models.
One step closer to unbiased aleatoric uncertainty estimation
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making.
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws.
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors.
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
A Survey on Role-Oriented Network Embedding
A wide variety of NE methods focus on the proximity of networks.
Robust Deep Reinforcement Learning through Adversarial Loss
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.
