Marin Soljacic

According to our database, Marin Soljacic authored at least 56 papers between 2013 and 2024.

Collaborative distances:

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

2024

Deep Learning and Symbolic Regression for Discovering Parametric Equations.

Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic

IEEE Trans. Neural Networks Learn. Syst., November, 2024

OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step.

Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljacic

CoRR, 2024

Stochastic logic in biased coupled photonic probabilistic bits.

Michael Horodynski, Charles Roques-Carmes, Yannick Salamin, Seou Choi, Jamison Sloan, Di Luo, Marin Soljacic

CoRR, 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 Soljacic

CoRR, 2024

KAN: Kolmogorov-Arnold Networks.

Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark

CoRR, 2024

TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net.

Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljacic, Di Luo

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks.

Ileana Rugina, Rumen Dangovski, Li Jing, Preslav Nakov, Marin Soljacic

Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

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

Trans. Mach. Learn. Res., 2023

Learning to Optimize Quasi-Newton Methods.

Isaac Liao, Rumen Dangovski, Jakob Nicolaus Foerster, Marin Soljacic

Trans. Mach. Learn. Res., 2023

Multimodal Learning for Crystalline Materials.

Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic

CoRR, 2023

Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulation.

Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic

CoRR, 2023

Model Stitching: Looking For Functional Similarity Between Representations.

Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic

CoRR, 2023

Geometry of contact: contact planning for multi-legged robots via spin models duality.

Baxi Chong, Di Luo, Tianyu Wang, Gabriel B. Margolis, Juntao He, Pulkit Agrawal, Marin Soljacic, Daniel I. Goldman

CoRR, 2023

QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning.

Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation.

Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Contextualizing Enhances Gradient Based Meta Learning for Few Shot Image Classification.

Evan Vogelbaum, Rumen Dangovski, Li Jing, Marin Soljacic

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

Meta-Learning and Self-Supervised Pretraining for Storm Event Imagery Translation.

Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

Manifold Transfer Networks for Lens Distortion Rectification.

Li Jing, Lay Jain, Rumen Dangovski, Marin Soljacic

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

Asymmetric Grouped Convolutions for Logarithmic Scale Efficient Convolutional Neural Networks.

Li Jing, Rumen Dangovski, Marin Soljacic

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

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

Trans. Mach. Learn. Res., 2022

Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning.

Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

CoRR, 2022

On the Importance of Calibration in Semi-supervised Learning.

Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava

CoRR, 2022

AI-Assisted Discovery of Quantitative and Formal Models in Social Science.

Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljacic

CoRR, 2022

Discovering Conservation Laws using Optimal Transport and Manifold Learning.

Peter Y. Lu, Rumen Dangovski, Marin Soljacic

CoRR, 2022

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials.

Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljacic

CoRR, 2022

DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings.

Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Scott Yih, Yoon Kim, James R. Glass

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery.

Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic

IEEE Trans. Neural Networks Learn. Syst., 2021

Meta-Learning and Self-Supervised Pretraining for Real World Image Translation.

Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic

CoRR, 2021

Equivariant Contrastive Learning.

Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

CoRR, 2021

Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science.

Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic

CoRR, 2021

Discovering Sparse Interpretable Dynamics from Partial Observations.

Peter Y. Lu, Joan Ariño, Marin Soljacic

CoRR, 2021

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems.

Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

CoRR, 2021

Non-Abelian gauge fields with fiber optics and beyond.

Yi Yang, Marin Soljacic

Proceedings of the Optical Fiber Communications Conference and Exhibition, 2021

Adapting Deep Learning Models to New Meteorological Contexts Using Transfer Learning.

Pooya Khorrami, Olga Simek, Brian Cheung, Mark Veillette, Rumen Dangovski, Ileana Rugina, Marin Soljacic, Pulkit Agrawal

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs.

Allan dos Santos Costa, Rumen Dangovski, Samuel Kim, Pawan Goyal, Marin Soljacic, Joseph Jacobson

CoRR, 2020

Contextualizing Enhances Gradient Based Meta Learning.

Evan Vogelbaum, Rumen Dangovski, Li Jing, Marin Soljacic

CoRR, 2020

On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning.

Guillem Ramírez, Rumen Dangovski, Preslav Nakov, Marin Soljacic

CoRR, 2020

2019

Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications.

Rumen Dangovski, Li Jing, Preslav Nakov, Mico Tatalovic, Marin Soljacic

Trans. Assoc. Comput. Linguistics, 2019

Gated Orthogonal Recurrent Units: On Learning to Forget.

Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio

Neural Comput., 2019

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning.

Peter Y. Lu, Samuel Kim, Marin Soljacic

CoRR, 2019

2018

Large-Scale Optical Neural Networks based on Photoelectric Multiplication.

Ryan Hamerly, Alex Sludds, Liane Bernstein, Marin Soljacic, Dirk R. Englund

CoRR, 2018

WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices.

Li Jing, Rumen Dangovski, Marin Soljacic

CoRR, 2018

Photonic Recurrent Ising Sampler.

Charles Roques-Carmes, Yichen Shen, Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubcek, Vladimir Ceperic, John D. Joannopoulos, Dirk R. Englund, Marin Soljacic

CoRR, 2018

Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks.

Yurui Qu, Li Jing, Yichen Shen, Min Qiu, Marin Soljacic

CoRR, 2018

On-Chip Optical Convolutional Neural Networks.

Hengameh Bagherian, Scott A. Skirlo, Yichen Shen, Huaiyu Meng, Vladimir Ceperic, Marin Soljacic

CoRR, 2018

Controlling the Near-Field of Metasurfaces for Free-Electron Multi-Harmonic Hard X-Ray Generation.

Gilles Rosolen, Liang Jie Wong, Nicholas Rivera, Bjorn Maes, Marin Soljacic, Ido Kaminer

Proceedings of the 2018 20th International Conference on Transparent Optical Networks (ICTON), 2018

Rotational Unit of Memory.

Rumen Dangovski, Li Jing, Marin Soljacic

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs.

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN.

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Max Tegmark, Marin Soljacic

CoRR, 2016

2014

Binary matrices of optimal autocorrelations as alignment marks.

Scott A. Skirlo, Ling Lu, Marin Soljacic

CoRR, 2014

2013

Plasmons in Graphene: Fundamental Properties and Potential Applications.

Marinko Jablan, Marin Soljacic, Hrvoje Buljan

Proc. IEEE, 2013