POSTER: Cutting the Fat: Speeding Up RBM for Fast Deep Learning Through Generalized Redundancy Elimination. - Lin Ning 001; Randall Pittman; Xipeng Shen (PACT, 2017)

Generalizations of the theory and deployment of triangular inequality for compiler-based strength reduction. - Yufei Ding 001; Lin Ning 001; Hui Guan 001; Xipeng Shen (PLDI, 2017)

LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine. - Lin Ning 001; Randall Pittman; Xipeng Shen (Neural Networks, 2018)

Adaptive Deep Reuse: Accelerating CNN Training on the Fly. - Lin Ning 001; Hui Guan 001; Xipeng Shen (ICDE, 2019)

Deep reuse: streamline CNN inference on the fly via coarse-grained computation reuse. - Lin Ning 001; Xipeng Shen (ICS, 2019)

In-Place Zero-Space Memory Protection for CNN. - Hui Guan 001; Lin Ning 001; Zhen Lin; Xipeng Shen; Huiyang Zhou; Seung-Hwan Lim (NeurIPS, 2019)

Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone. - Hui Guan 001; Umana Chaudhary; Yuanchao Xu 001; Lin Ning 001; Lijun Zhang 005; Xipeng Shen (ICDM, 2021)

Simple Augmentation Goes a Long Way: ADRL for DNN Quantization. - Lin Ning 001; Guoyang Chen; Weifeng Zhang 003; Xipeng Shen (ICLR, 2021)

Learning Federated Representations and Recommendations with Limited Negatives. - Lin Ning 001; Karan Singhal; Ellie X. Zhou; Sushant Prakash (CoRR, 2021)

What Do We Mean by Generalization in Federated Learning? - Honglin Yuan 002; Warren Richard Morningstar; Lin Ning 001; Karan Singhal (ICLR, 2022)

EANA: Reducing Privacy Risk on Large-scale Recommendation Models. - Lin Ning 001; Steve Chien; Shuang Song 001; Mei Chen; Yunqi Xue; Devora Berlowitz (RecSys, 2022)

Mixed Federated Learning: Joint Decentralized and Centralized Learning. - Sean Augenstein; Andrew Hard; Lin Ning 001; Karan Singhal; Satyen Kale; Kurt Partridge; Rajiv Mathews (CoRR, 2022)

General Reuse-Centric CNN Accelerator. - Nihat Mert Cicek; Lin Ning 001; Ozcan Ozturk 001; Xipeng Shen (IEEE Trans. Computers, 2022)

UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches. - Chao Wang; Neo Wu; Lin Ning 001; Luyang Liu; Jun Xie; Shawn O'Banion; Bradley Green (CoRR, 2024)

RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs. - Jiaxing Wu; Lin Ning 001; Luyang Liu; Harrison Lee 001; Neo Wu; Chao Wang; Sushant Prakash; Shawn O'Banion; Bradley Green; Jun Xie (AAAI, 2025)

MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts. - Lin Ning 001; Harsh Lara; Meiqi Guo; Abhinav Rastogi (NAACL (Findings), 2025)

User-LLM: Efficient LLM Contextualization with User Embeddings. - Lin Ning 001; Luyang Liu; Jiaxing Wu; Neo Wu; Devora Berlowitz; Sushant Prakash; Bradley Green; Shawn O'Banion; Jun Xie (WWW (Companion Volume), 2025)

Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning. - Yuhan Liu; Lin Ning 001; Neo Wu; Karan Singhal; Philip Andrew Mansfield; Devora Berlowitz; Sushant Prakash; Bradley Green (CoRR, 2025)