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06/29/2023
Concept-Oriented Deep Learning with Large Language Models
Large Language Models (LLMs) have been successfully used in many natural...
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03/04/2023
Variational Quantum Classifiers for Natural-Language Text
As part of the recent research effort on quantum natural language proces...
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11/08/2022
Variational Quantum Kernels with Task-Specific Quantum Metric Learning
Quantum kernel methods, i.e., kernel methods with quantum kernels, offer...
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09/28/2022
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach
Quantum machine learning (QML) is the use of quantum computing for the c...
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07/16/2022
Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry
Deep learning for molecular science has so far mainly focused on 2D mole...
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05/13/2022
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs
Dual embodied-symbolic concept representations are the foundation for de...
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03/01/2022
Dual Embodied-Symbolic Concept Representations for Deep Learning
Motivated by recent findings from cognitive neural science, we advocate ...
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02/05/2022
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning
Humans are capable of learning new concepts from only a few (labeled) ex...
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12/10/2021
Concept Representation Learning with Contrastive Self-Supervised Learning
Concept-oriented deep learning (CODL) is a general approach to meet the ...
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07/14/2021
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers
Bayesian neural networks provide a direct and natural way to extend stan...
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06/22/2021
Bayesian Neural Networks: Essentials
Bayesian neural networks utilize probabilistic layers that capture uncer...
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05/31/2021
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertain...
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07/06/2020
Distance-Geometric Graph Convolutional Network (DG-GCN)
The distance-geometric graph representation adopts a unified scheme (dis...
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06/02/2020
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
The geometry of three-dimensional (3D) graphs, consisting of nodes and e...
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12/11/2019
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
Deep learning models are full of hyperparameters, which are set manually...
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10/24/2019
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Classification
Tiered graph autoencoders provide the architecture and mechanisms for le...
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08/22/2019
Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
Tiered latent representations and latent spaces for molecular graphs pro...
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02/11/2019
Probabilistic Generative Deep Learning for Molecular Design
Probabilistic generative deep learning for molecular design involves the...
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12/26/2018
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Latent representations are the essence of deep generative models and det...
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11/15/2018
Concept-Oriented Deep Learning: Generative Concept Representations
Generative concept representations have three major advantages over disc...
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06/05/2018