Search Results for author: Rong Zheng

Found 8 papers, 0 papers with code

DeepScale: An Online Frame Size Adaptation Approach to Accelerate Visual Multi-object Tracking

no code implementations22 Jul 2021 Keivan Nalaie, Rong Zheng

In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner.

Multi-Object Tracking

CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge

no code implementations3 Jul 2020 Yihao Fang, Shervin Manzuri Shalmani, Rong Zheng

Inference of uncompressed large scale DNN models can only run in the cloud with extra communication latency back and forth between cloud and end devices, while compressed DNN models achieve real-time inference on end devices at the price of lower predictive accuracy.

Image Classification Speech Recognition

SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge

no code implementations3 Oct 2019 Pooya Khandel, Amir Hossein Rassafi, Vahid Pourahmadi, Saeed Sharifian, Rong Zheng

As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node.

Object Classification

Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey

no code implementations11 Jan 2019 Chao Cai, Rong Zheng, Jun Luo

This framework encompasses three layers, i. e., physical layer, core technique layer, and application layer.

Logographic Subword Model for Neural Machine Translation

no code implementations7 Sep 2018 Yihao Fang, Rong Zheng, Xiaodan Zhu

A novel logographic subword model is proposed to reinterpret logograms as abstract subwords for neural machine translation.

Machine Translation

RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density

no code implementations2 Nov 2016 Yangli-ao Geng, Qingyong Li, Rong Zheng, Fuzhen Zhuangz, Ruisi He

Furthermore, we discover that the number of clusters computed by RECOME is a step function of the $\alpha$ parameter with jump discontinuity on a small collection of values.


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