Soljacic, Marin

20 publications

ICLR 2025 KAN: Kolmogorov–Arnold Networks Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark

NeurIPS 2025 L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling Zhuo Chen, Oriol Mayné i Comas, Zhuotao Jin, Di Luo, Marin Soljacic

NeurIPS 2024 OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step Owen Dugan, Donato M. Jiménez-Benetó, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljačić

NeurIPS 2024 QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić

ICML 2024 TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Nets Toward Machine Precision Zhuo Chen, Jacob Mccarran, Esteban Vizcaino, Marin Soljacic, Di Luo

NeurIPS 2023 ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic

TMLR 2023 Learning to Optimize Quasi-Newton Methods Isaac Liao, Rumen Dangovski, Jakob Nicolaus Foerster, Marin Soljacic

TMLR 2023 Mitigating Confirmation Bias in Semi-Supervised Learning via Efficient Bayesian Model Averaging Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

ICML 2023 Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava

ICML 2023 Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows Owen M Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic

NeurIPS 2023 QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

ICLRW 2023 Studying Phase Transitions in Contrastive Learning with Physics-Inspired Datasets Ali Cy, Anugrah Chemparathy, Michael Han, Rumen Dangovski, Peter Y. Lu, Marin Soljacic

NeurIPSW 2022 Data-Driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

ICMLW 2022 Deep Learning and Symbolic Regression for Discovering Parametric Equations Samuel Kim, Michael Zhang, Peter Y Lu, Marin Soljacic

TMLR 2022 Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim, Peter Y Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

ICLR 2022 Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

NeurIPSW 2022 Model Stitching: Looking for Functional Similarity Between Representations Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic

NeurIPSW 2021 Discovering Dynamical Parameters by Interpreting Echo State Networks Oreoluwa Alao, Peter Y Lu, Marin Soljacic

AAAI 2021 We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale Rumen Dangovski, Michelle Shen, Dawson Byrd, Li Jing, Desislava Tsvetkova, Preslav Nakov, Marin Soljacic

ICML 2017 Tunable Efficient Unitary Neural Networks (EUNN) and Their Application to RNNs Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljačić