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Found 10 papers, 4 papers with code
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks.
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs).
Pretrained Cost Model for Distributed Constraint Optimization Problems
Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to construct effective heuristics to boost a broad range of DCOP algorithms where evaluating the quality of a partial assignment is critical, such as local search or backtracking search.
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
We find that by using contrastive learning in the supervised setting, we can exploit label information effectively in a data-driven manner, and learn meaningful feature and label embeddings which capture the label correlations and enhance the predictive power.
Materials Representation and Transfer Learning for Multi-Property Prediction
To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
BIG-bench Machine Learning
Generative Adversarial Network
+4
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
This in turn leads to a challenging and long-standing problem in the field of computer science - how to perform ac-curate multi-label classification with hundreds of labels?
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distribution of species across landscapes over time.
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
The decoder of MPVAE takes in the samples from the embedding spaces and models the joint distribution of output targets under a Multivariate Probit model by learning a shared covariance matrix.
Multiagent Simple Temporal Problem: The Arc-Consistency Approach
The Simple Temporal Problem (STP) is a fundamental temporal reasoning problem and has recently been extended to the Multiagent Simple Temporal Problem (MaSTP).
Exploring Directional Path-Consistency for Solving Constraint Networks
Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention.
