A5074592048- Advanced Neural Network Applications
- Visual Attention and Saliency Detection
- Domain Adaptation and Few-Shot Learning
- Medical Image Segmentation Techniques
- Full-Duplex Wireless Communications
- Bacillus and Francisella bacterial research
- CCD and CMOS Imaging Sensors
- Cooperative Communication and Network Coding
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Adversarial Robustness in Machine Learning
- Machine Learning and ELM
- Advanced Image and Video Retrieval Techniques
- Advanced MIMO Systems Optimization
- Advanced Computing and Algorithms
- Direction-of-Arrival Estimation Techniques
- PAPR reduction in OFDM
- Brain Tumor Detection and Classification
- Wireless Communication Networks Research
- Multimodal Machine Learning Applications
- Blind Source Separation Techniques
- Cell Image Analysis Techniques
- COVID-19 diagnosis using AI
- Recommender Systems and Techniques
- Integrated Circuits and Semiconductor Failure Analysis
University of Wisconsin–Madison
2021-2024
Microsoft Research (United Kingdom)
2022
Beijing University of Posts and Telecommunications
2016-2017
Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT suffer from huge number of parameters, restricting applicability on devices with limited memory. To alleviate this problem, we propose MiniViT, a new compression framework, which achieves parameter reduction transformers while retaining the same performance. The central idea MiniViT is multiplex weights consecutive transformer blocks. More specifically, make...
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which one of the most typical machine learning tasks personalized advertising and recommender systems. Although developing hand-crafted effective for small number datasets, it generally requires laborious tedious architecture engineering extensive scenarios. In recent years, several neural search (NAS) methods have been proposed designing automatically. However, existing only explore limited types...
Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability. However, most prevailing ViT models suffer from huge number of parameters, restricting their applicability on devices with limited resources. To alleviate this issue, we propose TinyViT, a new family tiny and efficient small transformers pretrained large-scale datasets our proposed fast distillation framework. The central idea is transfer knowledge large ones, while enabling...
Conventional amplify-and-forward (AF) protocol for half-duplex two-hop multiple-input-multiple-output (MIMO) relay systems assumes that the source node transmits signal only at first time slot. While making silent second slot simplifies system design, it is strictly suboptimal. To improve performance, in this paper, we consider signals during both slots. We develop two novel iterative algorithms to optimize source, relay, and receiver matrices new AF MIMO system. Both are based on minimum...
This research introduces BAE-ViT, a specialized vision transformer model developed for bone age estimation (BAE). is designed to efficiently merge image and sex data, capability not present in traditional convolutional neural networks (CNNs). BAE-ViT employs novel data fusion method facilitate detailed interactions between visual non-visual by tokenizing information concatenating all tokens (visual or non-visual) as the input model. The underwent training on large-scale dataset from 2017...
The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with increasing depth the network, which is a major constraint for efficient network training on modern GPUs limited memory, embedded systems, and mobile devices. Several studies show that feature maps (as generated after convolutional layers) are main bottleneck in this problem. Often, these mimic natural photographs sense their energy concentrated spectral domain. Although embedding CNN do-main...
The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with increasing depth the network, which is a major constraint for efficient network training on modern GPUs limited memory, embedded systems, and mobile devices. Several studies show that feature maps (as generated after convolutional layers) are main bottleneck in this problem. Often, these mimic natural photographs sense their energy concentrated spectral domain. Although embedding CNN domain...
Generalized Frequency Division Multiplexing (GFDM) is a promising solution for the cellular system of fifth generation (5G) PHY layer because its flexibility can address different application requirements. However, due to pulse shaping, there inherent interference existing in received signal, which has negative impact on pilot-based channel estimation. Although pilot symbols be eliminated at transmitter by using precoding matrices, accompanied transmitting power penalty increases with...
Abstract Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the evaluates traditional models RobMedNAS-optimized under attacks. Findings demonstrate RobMedNAS’s efficacy...
Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT suffer from huge number of parameters, restricting applicability on devices with limited memory. To alleviate this problem, we propose MiniViT, a new compression framework, which achieves parameter reduction transformers while retaining the same performance. The central idea MiniViT is multiplex weights consecutive transformer blocks. More specifically, make...
Purpose: The purpose of this study is to investigate the robustness a commonly-used convolutional neural network for image segmentation with respect visually-subtle adversarial perturbations, and suggest new methods make these networks more robust such perturbations. Materials Methods: In retrospective study, accuracy brain tumor was studied in subjects low- high-grade gliomas. A three-dimensional UNet model implemented segment four different MR series (T1-weighted, post-contrast...
In orthogonal frequency-division multiplexing access (OFDMA) system, a distributed Bayesian compressive sensing (DBCS) based blind carrier frequency offset (CFO) estimator has been proposed, which offers significant improvement on the performance of multiple-parameter estimation, compared with existing subspace theory method. However, analysis for theoretical and computational complexity is absent. this paper, we conduct further study method, derive Cramer-Rao Bound to evaluate performance....
In this paper, we study the transceiver design for two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems using a new strategy, in which source sends data at both time intervals. We propose alternating approach to and matrices based on minimum mean-squared error (MMSE) criterion. show through numerical simulations that proposed system has lower bit-error-rate compared with existing MIMO AF systems.