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Found 19 papers, 6 papers with code
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition
Radio-frequency (RF)-based human activity recognition (HAR) provides a contactless and privacy-preserving solution for monitoring human behavior in applications such as personalized healthcare, ambient assisted living, and telemedicine.
A Large-dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT
In this paper, we perform asymptotic analyses of the widely used ESPRIT direction-of-arrival (DoA) estimator for large arrays, where the array size $N$ and the number of snapshots $T$ grow to infinity at the same pace.
TRIS-HAR: Transmissive Reconfigurable Intelligent Surfaces-assisted Cognitive Wireless Human Activity Recognition Using State Space Models
Human activity recognition (HAR) using radio frequency (RF) signals has garnered considerable attention for its applications in smart environments.
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks.
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query
In DeLR, the query is based on region-level, and we only annotate the object region that is queried; 2) Instead of directly providing both localization and recognition annotations, we separately query the two components, and thus reduce the recognition budget with the pseudo class labels provided by the model.
RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization
The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS).
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport
To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment.
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
Implicit neural networks have demonstrated remarkable success in various tasks.
Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment
The use of denoising diffusion models is becoming increasingly popular in the field of image editing.
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.
Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images
It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images.
One-Bit Active Query With Contrastive Pairs
The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling.
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.
Spectrum concentration in deep residual learning: a free probability approach
We revisit the initialization of deep residual networks (ResNets) by introducing a novel analytical tool in free probability to the community of deep learning.
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
The location of broken insulators in aerial images is a challenging task.
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties.
