Caleb Geniesse

According to our database, Caleb Geniesse authored at least 11 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis.

Jiaqing Chen, Nicholas Hadler, Tiankai Xie, Rostyslav Hnatyshyn, Caleb Geniesse, Yaoqing Yang, Michael W. Mahoney, Talita Perciano, John F. Hartwig, Ross Maciejewski, Gunther H. Weber

CoRR, February, 2026

2025

Removing Watermarks with Partial Regeneration using Semantic Information.

Krti Tallam, John Kevin Cava, Caleb Geniesse, N. Benjamin Erichson, Michael W. Mahoney

CoRR, May, 2025

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing.

Tommaso Baldi, Javier Campos, Olivia Weng, Caleb Geniesse, Nhan Tran, Ryan Kastner, Alessandro Biondi

CoRR, February, 2025

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics.

Tiankai Xie, Jiaqing Chen, Yaoqing Yang, Caleb Geniesse, Ge Shi, Ajinkya Chaudhari, John Kevin Cava, Michael W. Mahoney, Talita Perciano, Gunther H. Weber, Ross Maciejewski

IEEE Computer Graphics and Applications, 2025

Mitigating Memorization in Language Models.

Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C. Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT.

Olivia Weng, Marta Andronic, Danial Zuberi, Jiaqing Chen, Caleb Geniesse, George A. Constantinides, Nhan Tran, Nicholas J. Fraser, Javier Mauricio Duarte, Ryan Kastner

Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2025

2024

Visualizing Loss Functions as Topological Landscape Profiles.

Caleb Geniesse, Jiaqing Chen, Tiankai Xie, Ge Shi, Yaoqing Yang, Dmitriy Morozov, Talita Perciano, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber

CoRR, 2024

Evaluating Loss Landscapes from a Topology Perspective.

Tiankai Xie, Caleb Geniesse, Jiaqing Chen, Yaoqing Yang, Dmitriy Morozov, Michael W. Mahoney, Ross Maciejewski, Gunther H. Weber

CoRR, 2024

Reliable edge machine learning hardware for scientific applications.

Tommaso Baldi, Javier Campos, Benjamin Hawks, Jennifer Ngadiuba, Nhan Tran, Daniel Diaz, Javier M. Duarte, Ryan Kastner, Andres Meza, Melissa Quinnan, Olivia Weng, Caleb Geniesse, Amir Gholami, Michael W. Mahoney, Vladimir Loncar, Philip C. Harris, Joshua Agar, Shuyu Qin

Proceedings of the 42nd IEEE VLSI Test Symposium, 2024

2021

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking.

Hua Xie, Roger E. Beaty, Sahar Jahanikia, Caleb Geniesse, Neeraj S. Sonalkar, Manish Saggar

NeuroImage, 2021

2017

MoleculeNet: A Benchmark for Molecular Machine Learning.

Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay S. Pande

CoRR, 2017