Search Results for author: Geng Zhao

Found 8 papers, 3 papers with code

Welfare Distribution in Two-sided Random Matching Markets

no code implementations16 Feb 2023 Itai Ashlagi, Mark Braverman, Geng Zhao

In the model, each agent has a latent personal score for every agent on the other side of the market and her preferences follow a logit model based on these scores.

Vocal Bursts Valence Prediction

Online Learning in Stackelberg Games with an Omniscient Follower

no code implementations27 Jan 2023 Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan

We analyze the sample complexity of regret minimization in this repeated Stackelberg game.

Memristive Computing for Efficient Inference on Resource Constrained Devices

no code implementations21 Aug 2022 Venkatesh Rammamoorthy, Geng Zhao, Bharathi Reddy, Ming-Yang Lin

The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied.

Aesthetic Attributes Assessment of Images

2 code implementations11 Jul 2019 Xin Jin, Le Wu, Geng Zhao, Xiao-Dong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou

This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.

Attribute Image Captioning +1

Multi-level Chaotic Maps for 3D Textured Model Encryption

no code implementations25 Sep 2017 Xin Jin, Shuyun Zhu, Le Wu, Geng Zhao, Xiao-Dong Li, Quan Zhou, Huimin Lu

In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures.

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

2 code implementations23 Aug 2017 Xin Jin, Le Wu, Xiao-Dong Li, Siyu Chen, Siwei Peng, Jingying Chi, Shiming Ge, Chenggen Song, Geng Zhao

Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).

ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation

2 code implementations7 Oct 2016 Xin Jin, Le Wu, Xiao-Dong Li, Xiaokun Zhang, Jingying Chi, Siwei Peng, Shiming Ge, Geng Zhao, Shuying Li

Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.

Domain Adaptation General Classification +2

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