Deep Compression of Sum-Product Networks on Tensor Networks. - Ching Yun Ko; Cong Chen 003; Yuke Zhang; Kim Batselier; Ngai Wong 001 (CoRR, 2018)

A Reconfigurable Passive Switched-Capacitor Multiply-and-Accumulate Unit for Approximate Computing. - Yuke Zhang; Dina El-Damak (MWSCAS, 2020)

Deep Model Compression and Inference Speedup of Sum-Product Networks on Tensor Trains. - Ching-Yun Ko; Cong Chen 003; Zhuolun He; Yuke Zhang; Kim Batselier; Ngai Wong 001 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Fun-SAT: Functional Corruptibility-Guided SAT-Based Attack on Sequential Logic Encryption. - Yinghua Hu; Yuke Zhang; Kaixin Yang; Dake Chen; Peter A. Beerel; Pierluigi Nuzzo 002 (HOST, 2021)

Extremal problems on distance spectra of graphs. - Huiqiu Lin; Yuke Zhang (Discret. Appl. Math., 2021)

Perfect matching and distance spectral radius in graphs and bipartite graphs. - Yuke Zhang; Huiqiu Lin (Discret. Appl. Math., 2021)

TriLock: IC Protection with Tunable Corruptibility and Resilience to SAT and Removal Attacks. - Yuke Zhang; Yinghua Hu; Pierluigi Nuzzo 002; Peter A. Beerel (DATE, 2022)

Toward Continuous Finger Positioning on Ear Using Bone Conduction Speaker. - Yuke Zhang; Ken Takaki; Hiroaki Murakami; Takuya Sasatani; Yoshihiro Kawahara (SenSys, 2022)

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter. - Liangjie Jia; Peng Rao; Yuke Zhang; Yueqi Su; Xin Chen (Sensors, 2022)

Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference. - Souvik Kundu 002; Yuke Zhang; Dake Chen; Peter A. Beerel (CVPR Workshops, 2023)

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Haomei Liu; Ruiheng Peng; Peter A. Beerel (DAC, 2023)

RNA-ViT: Reduced-Dimension Approximate Normalized Attention Vision Transformers for Latency Efficient Private Inference. - Dake Chen; Yuke Zhang; Souvik Kundu 002; Chenghao Li; Peter A. Beerel (ICCAD, 2023)

SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Chenghao Li; Peter A. Beerel (ICCV, 2023)

Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference. - Souvik Kundu 002; Shunlin Lu; Yuke Zhang; Jacqueline Tiffany Liu; Peter A. Beerel (ICLR, 2023)

Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP. - Dake Chen; Xuan Zhou; Yinghua Hu; Yuke Zhang; Kaixin Yang; Andrew Rittenbach; Pierluigi Nuzzo 002; Peter A. Beerel (ISQED, 2023)

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Haomei Liu; Ruiheng Peng; Peter A. Beerel (CoRR, 2023)

Graphs with three distinct distance eigenvalues. - Yuke Zhang; Huiqiu Lin (Appl. Math. Comput., 2023)

Dim Moving Multi-Target Enhancement with Strong Robustness for False Enhancement. - Yuke Zhang; Xin Chen; Peng Rao; Liangjie Jia (Remote. Sens., 2023)

On the Security of Sequential Logic Locking Against Oracle-Guided Attacks. - Yinghua Hu; Yuke Zhang; Kaixin Yang; Dake Chen; Peter A. Beerel; Pierluigi Nuzzo 002 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

DIA: Diffusion based Inverse Network Attack on Collaborative Inference. - Dake Chen; Shiduo Li; Yuke Zhang; Chenghao Li; Souvik Kundu 002; Peter A. Beerel (CVPR Workshops, 2024)

Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement. - Chenghao Li; Dake Chen; Yuke Zhang; Peter A. Beerel (ICASSP, 2024)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems. - Jacob Huckelberry; Yuke Zhang; Allison Sansone; James Mickens; Peter A. Beerel; Vijay Janapa Reddi (CoRR, 2024)

LoyalDiffusion: A Diffusion Model Guarding Against Data Replication. - Chenghao Li; Yuke Zhang; Dake Chen; Jingqi Xu; Peter A. Beerel (CoRR, 2024)

A Weighted Guided Filtering-Based Multidomain Fusion Destriping Method. - Yang Hong; Peng Rao; Yuxing Zhou; Yuke Zhang (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

Maximizing the degree powers of graphs with fixed size. - Liwen Zhang; Yuke Zhang (Discret. Appl. Math., 2024)

The sum of the k largest distance eigenvalues of graphs. - Yuke Zhang; Huiqiu Lin (Discret. Math., 2024)

h-extra l-component connectivity of graphs and digraphs with minimum distance spectral radii. - Yanling Hu; Huiqiu Lin; Yuke Zhang (Discret. Math., 2025)

Rainbow Hamiltonicity and the spectral radius. - Yuke Zhang; Edwin R. van Dam (Discret. Math., 2025)

LF-HGRILF: A law-fact heterogeneous graph representation and iterative learning framework for legal judgment prediction. - Yinying Kong; You-Gan Wang; Haodong Deng; Zhanhao Xiao; Yuke Zhang (Knowl. Based Syst., 2025)

Diverse Inference and Verification for Advanced Reasoning. - Iddo Drori; Gaston Longhitano; Mao Mao; Seunghwan Hyun; Yuke Zhang; Sungjun Park; Zachary Meeks; Xin-Yu Zhang; Ben Segev; Howard Yong; Nakul Verma; Avi Shporer; et al. (CoRR, 2025)

Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study. - Yuke Zhang (CoRR, 2025)

FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings. - Jingqi Xu; Chenghao Li; Yuke Zhang; Peter A. Beerel (CoRR, 2025)