5,280 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
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints
Objective: The purpose of this manuscript is to accelerate cardiac diffusion
tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Methods: Diffusion-weighted images exhibit both transform sparsity and
low-rankness. These properties can jointly be exploited to accelerate CDTI,
especially when a phase map is applied to correct for the phase inconsistency
across diffusion directions, thereby enhancing low-rankness. The proposed
method is evaluated both ex vivo and in vivo, and is compared to methods using
either a low-rank or sparsity constraint alone. Results: Compared to using a
low-rank or sparsity constraint alone, the proposed method preserves more
accurate helix angle features, the transmural continuum across the myocardium
wall, and mean diffusivity at higher acceleration, while yielding significantly
lower bias and higher intraclass correlation coefficient. Conclusion:
Low-rankness and compressed sensing together facilitate acceleration for both
ex vivo and in vivo CDTI, improving reconstruction accuracy compared to
employing either constraint alone. Significance: Compared to previous methods
for accelerating CDTI, the proposed method has the potential to reach higher
acceleration while preserving myofiber architecture features which may allow
more spatial coverage, higher spatial resolution and shorter temporal footprint
in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical
Engineerin
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