6,405 research outputs found
Concept-Oriented Deep Learning with Large Language Models
Large Language Models (LLMs) have been successfully used in many
natural-language tasks and applications including text generation and AI
chatbots. They also are a promising new technology for concept-oriented deep
learning (CODL). However, the prerequisite is that LLMs understand concepts and
ensure conceptual consistency. 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. Human knowledge consists of both
symbolic (conceptual) knowledge and embodied (sensory) knowledge. Text-only
LLMs, however, can represent only symbolic (conceptual) knowledge. Multimodal
LLMs, on the other hand, are capable of representing the full range (conceptual
and sensory) of human knowledge. We discuss conceptual understanding in
visual-language LLMs, the most important multimodal LLMs, and major uses of
them for CODL including concept extraction from image, concept graph extraction
from image, and concept learning. While uses of LLMs for CODL are valuable
standalone, they are particularly valuable as part of LLM applications such as
AI chatbots
Variational Quantum Kernels with Task-Specific Quantum Metric Learning
Quantum kernel methods, i.e., kernel methods with quantum kernels, offer
distinct advantages as a hybrid quantum-classical approach to quantum machine
learning (QML), including applicability to Noisy Intermediate-Scale Quantum
(NISQ) devices and usage for solving all types of machine learning problems.
Kernel methods rely on the notion of similarity between points in a higher
(possibly infinite) dimensional feature space. For machine learning, the notion
of similarity assumes that points close in the feature space should be close in
the machine learning task space. In this paper, we discuss the use of
variational quantum kernels with task-specific quantum metric learning to
generate optimal quantum embeddings (a.k.a. quantum feature encodings) that are
specific to machine learning tasks. Such task-specific optimal quantum
embeddings, implicitly supporting feature selection, are valuable not only to
quantum kernel methods in improving the latter's performance, but they can also
be valuable to non-kernel QML methods based on parameterized quantum circuits
(PQCs) as pretrained embeddings and for transfer learning. This further
demonstrates the quantum utility, and quantum advantage (with
classically-intractable quantum embeddings), of quantum kernel methods
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