Duc Duy Nguyen,Lam Thanh Nguyen,Yifeng Huang et al.
Duc Duy Nguyen et al.
We present Class-agnostic Repetitive action Counting (CaRaCount), a novel approach to count repetitive human actions in the wild using wearable devices time series data. CaRaCount is the first few-shot class-agnostic method, being able to c...
Zongbo Bao,Penghui Yao
Zongbo Bao
We consider the problems of testing and learning quantum -junta channels, which are -qubit to -qubit quantum channels acting non-trivially on at most out of qubits and leaving the rest of qubits unchanged. We show the following. 1) An -quer...
On the Upper Bounds of Number of Linear Regions and Generalization Error of Deep Convolutional Neural Networks [0.03%]
深度卷积神经网络线性区域数量和泛化误差上限的研究
Degang Chen,Jiayu Liu,Xiaoya Che
Degang Chen
Understanding the effect of hyperparameters of the network structure on the performance of Convolutional Neural Networks (CNNs) remains the most fundamental and urgent issue in deep learning, and we attempt to address this issue based on th...
Elias Ramzi,Nicolas Audebert,Clement Rambour et al.
Elias Ramzi et al.
In image retrieval, standard evaluation metrics rely on score ranking, e.g. average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work, we introduce a general framework for robust and decomposable ...
A Variational Bayesian Inference Theory of Elasticity and its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension [0.03%]
弹性变分贝叶斯推理理论及其混合概率有限元方法在任意维度下的反变形解
Chao Wang,Shaofan Li
Chao Wang
In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) that can be used to solve the inverse deformation p...
Lorenzo Cappello,Oscar Hernan Madrid Padilla
Lorenzo Cappello
This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point locations and providing a scalable algorithm for inference. We do th...
Hyeon Jeon,Michael Aupetit,DongHwa Shin et al.
Hyeon Jeon et al.
Clustering techniques are often validated using benchmark datasets where class labels are used as ground-truth clusters. However, depending on the datasets, class labels may not align with the actual data clusters, and such misalignment ham...
Lingkun Luo,Shiqiang Hu,Liming Chen
Lingkun Luo
In domain adaptation (DA), the effectiveness of deep learning-based models is often constrained by batch learning strategies that fail to fully apprehend the global statistical and geometric characteristics of data distributions. Addressing...
Shared Growth of Graph Neural Networks Via Prompted Free-Direction Knowledge Distillation [0.03%]
通过提示的自由方向知识蒸馏实现图神经网络的共享增长
Kaituo Feng,Yikun Miao,Changsheng Li et al.
Kaituo Feng et al.
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is often...
Frozen CLIP-DINO: a Strong Backbone for Weakly Supervised Semantic Segmentation [0.03%]
冻结的CLIP-DINO：弱监督语义分割的强大骨干网络
Bingfeng Zhang,Siyue Yu,Jimin Xiao et al.
Bingfeng Zhang et al.
Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model, while there is no attemp...