Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices. - Byung Hoon Ahn; Jinwon Lee; Jamie Menjay Lin; Hsin-Pai Cheng; Jilei Hou; Hadi Esmaeilzadeh (MLSys, 2020)

Structured Convolutions for Efficient Neural Network Design. - Yash Bhalgat; Yizhe Zhang 001; Jamie Menjay Lin; Fatih Porikli (NeurIPS, 2020)

Phase Selective Convolution. - Jamie Menjay Lin; Parham Noorzad; Yang Yang 010; Nojun Kwak; Fatih Porikli (CVPR Workshops, 2021)

Imposing Consistency for Optical Flow Estimation. - Jisoo Jeong; Jamie Menjay Lin; Fatih Porikli; Nojun Kwak (CVPR, 2022)

Efficient Heterogeneous Video Segmentation at the Edge. - Jamie Menjay Lin; Siargey Pisarchyk; Juhyun Lee; David Tian; Tingbo Hou; Karthik Raveendran; Raman Sarokin; George Sung; Trent Tolley; Matthias Grundmann (CoRR, 2022)

DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow. - Risheek Garrepalli; Jisoo Jeong; Rajeswaran C. Ravindran; Jamie Menjay Lin; Fatih Porikli (CVPR Workshops, 2023)

Guided Speech Enhancement Network. - Yang Yang 010; Shao-Fu Shih; Hakan Erdogan; Jamie Menjay Lin; Chehung Lee; Yunpeng Li; George Sung; Matthias Grundmann (ICASSP, 2023)

OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation. - Jisoo Jeong; Hong Cai; Risheek Garrepalli; Jamie Menjay Lin; Munawar Hayat; Fatih Porikli (CVPR, 2024)

SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations. - Jamie Menjay Lin; Jisoo Jeong; Hong Cai; Risheek Garrepalli; Kai Wang; Fatih Porikli (CVPR Workshops, 2024)

Improving Optical Flow and Stereo Depth Estimation by Leveraging Uncertainty-Based Learning Difficulties. - Jisoo Jeong; Hong Cai; Jamie Menjay Lin; Fatih Porikli (CVPR Workshops, 2025)