On the Complexity Reduction of Dense Layers from O(N2) to O(NlogN) with Cyclic Sparsely Connected Layers. - Morteza Hosseini; Mark Horton; Hiren Paneliya; Uttej Kallakuri; Houman Homayoun; Tinoosh Mohsenin (DAC, 2019)

On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning. - Bharat Prakash; Mark Horton; Nicholas R. Waytowich; William David Hairston; Tim Oates 001; Tinoosh Mohsenin (ACM Great Lakes Symposium on VLSI, 2019)

A Flexible Multichannel EEG Artifact Identification Processor using Depthwise-Separable Convolutional Neural Networks. - Mohit Khatwani; Hasib-Al Rashid; Hirenkumar Paneliya; Mark Horton; Nicholas R. Waytowich; W. David Hairston; Tinoosh Mohsenin (ACM J. Emerg. Technol. Comput. Syst., 2021)

DefT: Boosting Scalability of Deformable Convolution Operations on GPUs. - Edward Hanson; Mark Horton; Hai (Helen) Li; Yiran Chen 001 (ASPLOS (3), 2023)

Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency. - Bhavna Gopal; Huanrui Yang; Jingyang Zhang; Mark Horton; Yiran Chen 001 (CoRR, 2024)

SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers. - Bhavna Gopal; Huanrui Yang; Mark Horton; Yiran Chen 001 (CoRR, 2025)

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers. - Mark Horton; Tergel Molom-Ochir; Peter Liu; Bhavna Gopal; Chiyue Wei; Cong Guo 003; Brady Taylor; Deliang Fan; Shan X. Wang; Hai Li 001; Yiran Chen 001 (CoRR, 2025)