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Found 10 papers, 1 papers with code
Neural Networks Learn Statistics of Increasing Complexity
The distributional simplicity bias (DSB) posits that neural networks learn low-order moments of the data distribution first, before moving on to higher-order correlations.
Non-equilibrium molecular geometries in graph neural networks
Moreover, many of the properties that we aim to predict can often be obtained with little or no overhead on top of the DFT calculations used to produce the 3D geometry information, voiding the need for a predictive model.
Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks
Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants to mitigate climate change.
Counterfactual Regularization for Model-Based Reinforcement Learning
In sequential tasks, planning-based agents have a number of advantages over model-free agents, including sample efficiency and interpretability.
Description-Based Zero-shot Fine-Grained Entity Typing
Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text.
Learning Scripts as Hidden Markov Models
Scripts have been proposed to model the stereotypical event sequences found in narratives.
Open Set Learning with Counterfactual Images
In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training.
Event Detection with Neural Networks: A Rigorous Empirical Evaluation
Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts.
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
The exploration phase aims to select samples that shrink the search space as much as possible.
