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Few-shot intent detection with self-supervised pretraining and prototype-aware attention
Few-shot intent detection is a more challenging application. However, traditional prototypical
networks based on averaging often suffer from issues such as missing key information, poor …
networks based on averaging often suffer from issues such as missing key information, poor …
A framework for bilevel optimization on Riemannian manifolds
Bilevel optimization has gained prominence in various applications. In this study, we
introduce a framework for solving bilevel optimization problems, where the variables in both …
introduce a framework for solving bilevel optimization problems, where the variables in both …
AMMD: Attentive maximum mean discrepancy for few-shot image classification
Metric-based methods have attained promising performance for few-shot image
classification. Maximum Mean Discrepancy (MMD) is a typical distance between …
classification. Maximum Mean Discrepancy (MMD) is a typical distance between …
Classification of ground‐based auroral images by learning deep tensor feature representation on Riemannian manifold
Y Hu, Z Zhou, P Yang, X Zhao… - Journal of Geophysical …, 2024 - Wiley Online Library
Automatically classifying a huge amount of ground‐based auroral images is essential to
facilitate aurora morphology statistical research and aid in comprehending the …
facilitate aurora morphology statistical research and aid in comprehending the …
Learning from not-all-negative pairwise data and unlabeled data
A weakly-supervised approach utilizing data pairs with comparative or similarity/dissimilarity
information has gained popularity in various fields due to its cost-effectiveness. However, the …
information has gained popularity in various fields due to its cost-effectiveness. However, the …
Riemannian Bilevel Optimization
We develop new algorithms for Riemannian bilevel optimization. We focus in particular on
batch and stochastic gradient-based methods, with the explicit goal of avoiding second …
batch and stochastic gradient-based methods, with the explicit goal of avoiding second …
[HTML][HTML] Orthogonal Capsule Network with Meta-Reinforcement Learning for Small Sample Hyperspectral Image Classification
Most current hyperspectral image classification (HSIC) models require a large number of
training samples, and when the sample size is small, the classification performance …
training samples, and when the sample size is small, the classification performance …
Marginal debiased network for fair visual recognition
Deep neural networks (DNNs) are often prone to learn the spurious correlations between
target classes and bias attributes, like gender and race, inherent in a major portion of …
target classes and bias attributes, like gender and race, inherent in a major portion of …
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds
Meta-learning problem is usually formulated as a bi-level optimization in which the task-
specific and the meta-parameters are updated in the inner and outer loops of optimization …
specific and the meta-parameters are updated in the inner and outer loops of optimization …
[PDF][PDF] EMPHASIZING DATA QUALITY FOR THE IDENTIFICATION OF CHILI VARIETIES IN THE CONTEXT OF SMART AGRICULTURE.
ABSTRACT Aim/Purpose This research aims to evaluate models from meta-learning
techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic …
techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic …