DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. - Mingxi Cheng; Ji Li 006; Shahin Nazarian (ASP-DAC, 2018)

High performance training of deep neural networks using pipelined hardware acceleration and distributed memory. - Raghav Mehta; Yuyang Huang; Mingxi Cheng; Shrey Bagga; Nishant Mathur; Ji Li 006; Jeffrey Draper; Shahin Nazarian (ISQED, 2018)

H2O-Cloud: A Resource and Quality of Service-Aware Task Scheduling Framework for Warehouse-Scale Data Centers. - Mingxi Cheng; Ji Li 006; Paul Bogdan; Shahin Nazarian (CoRR, 2019)

VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text. - Mingxi Cheng; Shahin Nazarian; Paul Bogdan (WWW, 2020)

There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks. - Mingxi Cheng; Shahin Nazarian; Paul Bogdan (Frontiers Artif. Intell., 2020)

H₂O-Cloud: A Resource and Quality of Service-Aware Task Scheduling Framework for Warehouse-Scale Data Centers. - Mingxi Cheng; Ji Li 006; Paul Bogdan; Shahin Nazarian (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020)

A General Trust Framework for Multi-Agent Systems. - Mingxi Cheng; Chenzhong Yin; Junyao Zhang; Shahin Nazarian; Jyotirmoy Deshmukh; Paul Bogdan (AAMAS, 2021)

Trust-aware Control for Intelligent Transportation Systems. - Mingxi Cheng; Junyao Zhang; Shahin Nazarian; Jyotirmoy Deshmukh; Paul Bogdan (IV, 2021)

Trustworthiness Evaluation and Trust-Aware Design of CNN Architectures. - Mingxi Cheng; Tingyang Sun; Shahin Nazarian; Paul Bogdan (CoLLAs, 2022)

Generation-Based Data Augmentation Pipeline for Real-Time Automatic Gesture Recognition. - Mingxi Cheng; Fatima Zohra Daha; Amit Srivastava; Ji Li 006 (ICAART (2), 2023)

ClipCrop: Conditioned Cropping Driven by Vision-Language Model. - Zhihang Zhong; Mingxi Cheng; Zhirong Wu; Yuhui Yuan; Yinqiang Zheng; Ji Li 006; Han Hu 001; Stephen Lin 001; Yoichi Sato 001; Imari Sato (ICCV (Workshops), 2023)

Fractional dynamics foster deep learning of COPD stage prediction. - Chenzhong Yin; Mihai Udrescu; Gaurav Gupta; Mingxi Cheng; Andrei Lihu; Lucretia Udrescu; Paul Bogdan; David M. Mannino; Stefan Mihaicuta (CoRR, 2023)

Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives. - Chenzhong Yin; Mingxi Cheng; Xiongye Xiao; Xinghe Chen; Shahin Nazarian; Andrei Irimia; Paul Bogdan (CoRR, 2023)

Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks. - Anzhe Cheng; Heng Ping; Zhenkun Wang 008; Xiongye Xiao; Chenzhong Yin; Shahin Nazarian; Mingxi Cheng; Paul Bogdan (ICASSP, 2024)

Discovering Malicious Signatures in Software from Structural Interactions. - Chenzhong Yin; Hantang Zhang; Mingxi Cheng; Xiongye Xiao; Xinghe Chen; Xin Ren; Paul Bogdan (ICASSP, 2024)

Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning. - Xiongye Xiao; Gengshuo Liu; Gaurav Gupta; Defu Cao; Shixuan Li; Yaxing Li; Tianqing Fang; Mingxi Cheng; Paul Bogdan (ICLR, 2024)

Unsupervised Graphic Layout Grouping with Transformers. - Jialiang Zhu; Danqing Huang; Chunyu Wang 001; Mingxi Cheng; Ji Li 006; Han Hu 001; Xin Geng 001; Baining Guo (WACV, 2024)

Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization. - Zhanhao Liang; Yuhui Yuan; Shuyang Gu; Bohan Chen; Tiankai Hang; Mingxi Cheng; Ji Li; Liang Zheng (CVPR, 2025)