Search Results for author: Senem Velipasalar

Found 24 papers, 7 papers with code

Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning

1 code implementation12 Sep 2022 Feng Wang, M. Cenk Gursoy, Senem Velipasalar

In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches.

Federated Learning Image Classification +2

Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis

1 code implementation CVPR 2022 Jiajing Chen, Burak Kakillioglu, Huantao Ren, Senem Velipasalar

In order to address this issue and improve the performance of any baseline 3D point classification or segmentation model, we propose a new module, referred to as the Recycling MaxPooling (RMP) module, to recycle and utilize the features of some of the discarded points.

Point Cloud Classification Semantic Segmentation

Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

no code implementations8 Dec 2021 Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney

In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.

Anomaly Detection Decision Making

Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation

1 code implementation18 Nov 2021 Yantao Lu, Xuetao Hao, Yilan Li, Weiheng Chai, Shiqi Sun, Senem Velipasalar

It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for detection from a BEV.

3D Object Detection Autonomous Driving +2

Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation

no code implementations14 Nov 2021 Jiajing Chen, Burak Kakillioglu, Senem Velipasalar

As the core module of the DPFA-Net, we propose a Feature Aggregation layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism.

3D Object Classification Point Cloud Segmentation +1

Anomaly Detection via Controlled Sensing and Deep Active Inference

no code implementations12 May 2021 Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.

Anomaly Detection Decision Making

Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

no code implementations12 May 2021 Feng Wang, M. Cenk Gursoy, Senem Velipasalar

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications.

reinforcement-learning reinforcement Learning

PT-CapsNet: A Novel Prediction-Tuning Capsule Network Suitable for Deeper Architectures

no code implementations ICCV 2021 Chenbin Pan, Senem Velipasalar

Existing variations of CapsNets mainly focus on performance comparison with the original CapsNet, and have not outperformed CNN-based models on complex tasks.

object-detection Object Detection +1

Anomaly Detection and Sampling Cost Control via Hierarchical GANs

no code implementations28 Sep 2020 Chen Zhong, M. Cenk Gursoy, Senem Velipasalar

In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector.

Anomaly Detection Time Series

Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning

no code implementations12 Jul 2020 Feng Wang, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar

As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.

Decision Making reinforcement-learning +1

Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles

1 code implementation25 Jan 2020 Yilan Li, Senem Velipasalar

Several research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving.

Autonomous Driving object-detection +1

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

2 code implementations CVPR 2020 Yantao Lu, Yunhan Jia, Jian-Yu Wang, Bai Li, Weiheng Chai, Lawrence Carin, Senem Velipasalar

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.

Adversarial Attack Image Classification +3

Deep Actor-Critic Reinforcement Learning for Anomaly Detection

no code implementations28 Aug 2019 Chen Zhong, M. Cenk Gursoy, Senem Velipasalar

Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks.

Anomaly Detection reinforcement-learning +1

Autonomous Human Activity Classification from Ego-vision Camera and Accelerometer Data

no code implementations28 May 2019 Yantao Lu, Senem Velipasalar

For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is.

General Classification Multimodal Activity Recognition

Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction

1 code implementation8 May 2019 Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.

Image Classification object-detection +2

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

no code implementations31 Mar 2019 Chuang Ye, M. Cenk Gursoy, Senem Velipasalar

Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session.

reinforcement-learning reinforcement Learning

Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions

no code implementations16 Oct 2018 Chuang Ye, M. Cenk Gursoy, Senem Velipasalar

In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied.

Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access

no code implementations8 Oct 2018 Chen Zhong, Ziyang Lu, M. Cenk Gursoy, Senem Velipasalar

We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).

reinforcement-learning reinforcement Learning

Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm

no code implementations24 Sep 2018 Yantao Lu, Burak Kakillioglu, Senem Velipasalar

The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.

Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks

no code implementations24 May 2018 Burak Kakillioglu, Yantao Lu, Senem Velipasalar

Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.

Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS

no code implementations19 Oct 2015 Mehmet Emin Basbug, Koray Ozcan, Senem Velipasalar

With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently.

General Classification Time Series +1

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