Search Results for author:
Found 22 papers, 0 papers with code
Concept-Oriented Deep Learning with Large Language Models
We discuss these in this paper, as well as major uses of LLMs for CODL including concept extraction from text, concept graph extraction from text, and concept learning.
Variational Quantum Classifiers for Natural-Language Text
As part of the recent research effort on quantum natural language processing (QNLP), variational quantum sentence classifiers (VQSCs) have been implemented and supported in lambeq / DisCoPy, based on the DisCoCat model of sentence meaning.
Variational Quantum Kernels with Task-Specific Quantum Metric Learning
For machine learning, the notion of similarity assumes that points close in the feature space should be close in the machine learning task space.
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach
In this paper we discuss some important aspects of PQCs with quantum kernels including PQCs, quantum kernels, quantum kernels with quantum advantage, and the trainability of quantum kernels.
Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry
Deep learning for molecular science has so far mainly focused on 2D molecular graphs.
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs
We discuss the use of dual embodied-symbolic concept representations for molecular graph representation learning, specifically with exemplar-based contrastive self-supervised learning (SSL).
Dual Embodied-Symbolic Concept Representations for Deep Learning
As such, we further advocate the use of dual embodied-symbolic concept representations for deep learning.
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning
We discuss extending CSSL (1) to be based mainly on exemplars and only secondly on data augmentation, and (2) to apply to both unlabeled data (a large amount is available in general) and labeled data (a few exemplars can be obtained with valuable supervised knowledge).
Concept Representation Learning with Contrastive Self-Supervised Learning
Concept-oriented deep learning (CODL) is a general approach to meet the future challenges for deep learning: (1) learning with little or no external supervision, (2) coping with test examples that come from a different distribution than the training examples, and (3) integrating deep learning with symbolic AI.
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers
To support this, we propose hybrid Bayesian neural networks with functional probabilistic layers that encode function (and activation) uncertainty.
Bayesian Neural Networks: Essentials
Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural networks to support probabilistic deep learning.
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty.
Distance-Geometric Graph Convolutional Network (DG-GCN) for Three-Dimensional (3D) Graphs
To facilitate the incorporation of geometry in deep learning on 3D graphs, we propose a message-passing graph convolutional network based on the distance-geometric graph representation: DG-GCN (distance-geometric graph convolution network).
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 edges, plays a crucial role in many important applications.
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
To find the best configuration for these hyperparameters in such a high dimensional space, with time-consuming and expensive model training / validation, is not a trivial challenge.
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction
In this paper, we discuss the use of tiered graph autoencoders together with graph prediction for molecular graphs.
Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs
As a result of using tiered graph autoencoders for deep learning, each molecular graph possesses tiered latent representations.
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Flat latent representations (node embeddings or graph embeddings) fail to represent, and support the use of, groups.
Probabilistic Generative Deep Learning for Molecular Design
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach.
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Latent representations are the essence of deep generative models and determine their usefulness and power.
Concept-Oriented Deep Learning: Generative Concept Representations
Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning.
Concept-Oriented Deep Learning
Concepts are the foundation of human deep learning, understanding, and knowledge integration and transfer.
