PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems. - Alexander Potapov; Ian Colbert; Kenneth Kreutz-Delgado; Alexander Cloninger; Srinjoy Das (ACSSC, 2019)

AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks. - Ian Colbert; Kenneth Kreutz-Delgado; Srinjoy Das (IJCNN, 2019)

An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling. - Ian Colbert; Kenneth Kreutz-Delgado; Srinjoy Das (IEEE Access, 2021)

A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications? - Ian Colbert; Jake Daly; Kenneth Kreutz-Delgado; Srinjoy Das (CoRR, 2021)

Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework. - Siqiao Ruan; Ian Colbert; Kenneth Kreutz-Delgado; Srinjoy Das (CoRR, 2021)

Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations. - Xinyu Zhang 009; Ian Colbert; Kenneth Kreutz-Delgado; Srinjoy Das (CoRR, 2021)

Generating GPU Compiler Heuristics using Reinforcement Learning. - Ian Colbert; Jake Daly; Norm Rubin (CoRR, 2021)

Evaluating Navigation Behavior of Agents in Games using Non-Parametric Statistics. - Ian Colbert; Mehdi Saeedi (CoG, 2022)

Human-Like Navigation Behavior: A Statistical Evaluation Framework. - Ian Colbert; Mehdi Saeedi (CoRR, 2022)

Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries. - Alexander Cann; Ian Colbert; Ihab Amer (CoRR, 2022)

A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance. - Ian Colbert; Alessandro Pappalardo; Jakoba Petri-Koenig (ICCV, 2023)

Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance. - Ian Colbert; Alessandro Pappalardo; Jakoba Petri-Koenig (CoRR, 2023)

End-to-End Inference Optimization for Deep Learning-based Image Upsampling Networks - Ian Colbert (2023)

A2Q+: Improving Accumulator-Aware Weight Quantization. - Ian Colbert; Alessandro Pappalardo; Jakoba Petri-Koenig; Yaman Umuroglu (ICML, 2024)

Accumulator-Aware Post-Training Quantization. - Ian Colbert; Fabian Grob; Giuseppe Franco; Jinjie Zhang; Rayan Saab (CoRR, 2024)

Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs. - Aidan Ferguson; Perry Gibson; Lara D'Agata; Parker McLeod; Ferhat Yaman; Amitabh Das; Ian Colbert; José Cano 001 (EuroMLSys, 2025)

DPWatch: A Framework for Hardware-Based Differential Privacy Guarantees. - Pawan Kumar Sanjaya; Christina Giannoula; Ian Colbert; Ihab Amer; Mehdi Saeedi; Gabor Sines; Nandita Vijaykumar (IEEE Comput. Archit. Lett., 2025)

Improving Quantization with Post-Training Model Expansion. - Giuseppe Franco; Pablo Monteagudo-Lago; Ian Colbert; Nicholas J. Fraser; Michaela Blott (CoRR, 2025)

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization. - Shihao Zhang; Haoyu Zhang; Ian Colbert; Rayan Saab (CoRR, 2025)

Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach. - Daniel Campa; Mehdi Saeedi; Ian Colbert; Srinjoy Das (CoRR, 2025)