Marin Soljacic

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 62

Top Topics

Supervised Learning

Symbolic Regression

Top Venues

Trans. Mach. Learn. Res.

Publications

Michael Horodynski, Charles Roques-Carmes, Yannick Salamin, Seou Choi, Jamison Sloan, Di Luo, Marin Soljacic
Stochastic logic in biased coupled photonic probabilistic bits. CoRR (2024)
Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljacic, Di Luo
TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net. CoRR (2024)
Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark
KAN: Kolmogorov-Arnold Networks. CoRR (2024)
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljacic
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation. CoRR (2024)
Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljacic
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step. CoRR (2024)
Ileana Rugina, Rumen Dangovski, Li Jing, Preslav Nakov, Marin Soljacic
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks. LREC/COLING (2024)
Baxi Chong, Di Luo, Tianyu Wang, Gabriel Margolis, Juntao He, Pulkit Agrawal, Marin Soljacic, Daniel I. Goldman
Geometry of contact: contact planning for multi-legged robots via spin models duality. CoRR (2023)
Li Jing, Lay Jain, Rumen Dangovski, Marin Soljacic
Manifold Transfer Networks for Lens Distortion Rectification. HPEC (2023)
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML (2023)
Li Jing, Rumen Dangovski, Marin Soljacic
Asymmetric Grouped Convolutions for Logarithmic Scale Efficient Convolutional Neural Networks. HPEC (2023)
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging. Trans. Mach. Learn. Res. 2023 (2023)
Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic
Meta-Learning and Self-Supervised Pretraining for Storm Event Imagery Translation. HPEC (2023)
Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation. NeurIPS (2023)
Isaac Liao, Rumen Dangovski, Jakob Nicolaus Foerster, Marin Soljacic
Learning to Optimize Quasi-Newton Methods. Trans. Mach. Learn. Res. 2023 (2023)
Evan Vogelbaum, Rumen Dangovski, Li Jing, Marin Soljacic
Contextualizing Enhances Gradient Based Meta Learning for Few Shot Image Classification. HPEC (2023)
Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic
Multimodal Learning for Crystalline Materials. CoRR (2023)
Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic
Model Stitching: Looking For Functional Similarity Between Representations. CoRR (2023)
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. CoRR (2023)
Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. ICML (2023)
Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic
Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulation. CoRR (2023)
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning. NeurIPS (2023)
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR (2023)
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic
Deep Learning and Symbolic Regression for Discovering Parametric Equations. CoRR (2022)
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Scott Yih, Yoon Kim, James R. Glass
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings. NAACL-HLT (2022)
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR (2022)
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
On the Importance of Calibration in Semi-supervised Learning. CoRR (2022)
Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Wen-tau Yih, Yoon Kim, James R. Glass
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings. CoRR (2022)
Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic
Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning. CoRR (2022)
Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljacic
AI-Assisted Discovery of Quantitative and Formal Models in Social Science. CoRR (2022)
Peter Y. Lu, Rumen Dangovski, Marin Soljacic
Discovering Conservation Laws using Optimal Transport and Manifold Learning. CoRR (2022)
Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljacic
Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials. CoRR (2022)
Isaac Liao, Rumen R. Dangovski, Jakob N. Foerster, Marin Soljacic
Learning to Optimize Quasi-Newton Methods. CoRR (2022)
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. Trans. Mach. Learn. Res. 2022 (2022)
Yi Yang, Marin Soljacic
Non-Abelian gauge fields with fiber optics and beyond. OFC (2021)
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
Equivariant Contrastive Learning. CoRR (2021)
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. IEEE Trans. Neural Networks Learn. Syst. 32 (9) (2021)
Pooya Khorrami, Olga Simek, Brian Cheung, Mark Veillette, Rumen Dangovski, Ileana Rugina, Marin Soljacic, Pulkit Agrawal
Adapting Deep Learning Models to New Meteorological Contexts Using Transfer Learning. IEEE BigData (2021)
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science. CoRR (2021)
Rumen Dangovski, Michelle Shen, Dawson Byrd, Li Jing, Desislava Tsvetkova, Preslav Nakov, Marin Soljacic
We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale. AAAI (2021)
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. CoRR (2021)
Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation. CoRR (2021)
Peter Y. Lu, Joan Ariño, Marin Soljacic
Discovering Sparse Interpretable Dynamics from Partial Observations. CoRR (2021)
Ileana Rugina, Rumen Dangovski, Li Jing, Preslav Nakov, Marin Soljacic
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks. CoRR (2020)
Allan Costa, Rumen Dangovski, Samuel Kim, Pawan Goyal, Marin Soljacic, Joseph Jacobson
Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs. CoRR (2020)
Guillem Ramírez, Rumen Dangovski, Preslav Nakov, Marin Soljacic
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning. CoRR (2020)
Evan Vogelbaum, Rumen Dangovski, Li Jing, Marin Soljacic
Contextualizing Enhances Gradient Based Meta Learning. CoRR (2020)
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. CoRR (2019)
Rumen Dangovski, Li Jing, Preslav Nakov, Mico Tatalovic, Marin Soljacic
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications. Trans. Assoc. Comput. Linguistics 7 (2019)
Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio
Gated Orthogonal Recurrent Units: On Learning to Forget. Neural Comput. 31 (4) (2019)
Peter Y. Lu, Samuel Kim, Marin Soljacic
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning. CoRR (2019)
Gilles Rosolen, Liang Jie Wong, Nicholas Rivera, Bjorn Maes, Marin Soljacic, Ido Kaminer
Controlling the Near-Field of Metasurfaces for Free-Electron Multi-Harmonic Hard X-Ray Generation. ICTON (2018)
Rumen Dangovski, Li Jing, Marin Soljacic
Rotational Unit of Memory. ICLR (Workshop) (2018)
Ryan Hamerly, Alex Sludds, Liane Bernstein, Marin Soljacic, Dirk Englund
Large-Scale Optical Neural Networks based on Photoelectric Multiplication. CoRR (2018)
Hengameh Bagherian, Scott A. Skirlo, Yichen Shen, Huaiyu Meng, Vladimir Ceperic, Marin Soljacic
On-Chip Optical Convolutional Neural Networks. CoRR (2018)
Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio
Gated Orthogonal Recurrent Units: On Learning to Forget. AAAI Workshops (2018)
Yurui Qu, Li Jing, Yichen Shen, Min Qiu, Marin Soljacic
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks. CoRR (2018)
Charles Roques-Carmes, Yichen Shen, Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubcek, Vladimir Ceperic, John D. Joannopoulos, Dirk Englund, Marin Soljacic
Photonic Recurrent Ising Sampler. CoRR (2018)
Li Jing, Rumen Dangovski, Marin Soljacic
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices. CoRR (2018)
Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. ICML (2017)
Rumen Dangovski, Li Jing, Marin Soljacic
Rotational Unit of Memory. CoRR (2017)
Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio
Gated Orthogonal Recurrent Units: On Learning to Forget. CoRR (2017)
Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Max Tegmark, Marin Soljacic
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. CoRR (2016)
Scott A. Skirlo, Ling Lu, Marin Soljacic
Binary matrices of optimal autocorrelations as alignment marks. CoRR (2014)
Marinko Jablan, Marin Soljacic, Hrvoje Buljan
Plasmons in Graphene: Fundamental Properties and Potential Applications. Proc. IEEE 101 (7) (2013)