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Found 23 papers, 12 papers with code
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation.
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.
Mean-Variance Portfolio Selection in Contagious Markets
We consider a mean-variance portfolio selection problem in a financial market with contagion risk.
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.
Algorithmic insights on continual learning from fruit flies
We discovered a two layer neural circuit in the fruit fly olfactory system that addresses this challenge by uniquely combining sparse coding and associative learning.
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.
Graph Contrastive Learning Automated
Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data.
Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process
By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form.
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty
Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.
Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery.
Graph Contrastive Learning with Augmentations
In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.
When Does Self-Supervision Help Graph Convolutional Networks?
We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.
Network-principled deep generative models for designing drug combinations as graph sets
We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.
L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9. 5, 16. 9, 19. 3 and 5. 7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively.
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking.
Learning to Optimize in Swarms
Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.
Bayesian active learning for optimization and uncertainty quantification in protein docking
To the best of our knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment.
Egocentric Activity Prediction via Event Modulated Attention
Predicting future activities from an egocentric viewpoint is of particular interest in assisted living.
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI).
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
