Plasmons in Graphene: Fundamental Properties and Potential Applications. - Marinko Jablan; Marin Soljacic; Hrvoje Buljan (Proc. IEEE, 2013)

Binary matrices of optimal autocorrelations as alignment marks. - Scott A. Skirlo; Ling Lu; Marin Soljacic (CoRR, 2014)

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. - Li Jing 001; Yichen Shen 001; Tena Dubcek; John Peurifoy; Scott A. Skirlo; Max Tegmark; Marin Soljacic (CoRR, 2016)

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. - Li Jing 001; Yichen Shen 001; Tena Dubcek; John Peurifoy; Scott A. Skirlo; Yann LeCun; Max Tegmark; Marin Soljacic (ICML, 2017)

Rotational Unit of Memory. - Rumen Dangovski; Li Jing 001; Marin Soljacic (ICLR (Workshop), 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 (ICTON, 2018)

On-Chip Optical Convolutional Neural Networks. - Hengameh Bagherian; Scott A. Skirlo; Yichen Shen 001; Huaiyu Meng; Vladimir Ceperic; Marin Soljacic (CoRR, 2018)

Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks. - Yurui Qu; Li Jing 001; Yichen Shen 001; Min Qiu; Marin Soljacic (CoRR, 2018)

Photonic Recurrent Ising Sampler. - Charles Roques-Carmes; Yichen Shen 001; Cristian Zanoci; Mihika Prabhu; Fadi Atieh; Li Jing 001; Tena Dubcek; Vladimir Ceperic; John D. Joannopoulos; Dirk R. Englund; Marin Soljacic (CoRR, 2018)

WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices. - Li Jing 001; Rumen Dangovski; Marin Soljacic (CoRR, 2018)

Large-Scale Optical Neural Networks based on Photoelectric Multiplication. - Ryan Hamerly; Alex Sludds; Liane Bernstein; Marin Soljacic; Dirk R. Englund (CoRR, 2018)

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning. - Peter Y. Lu; Samuel Kim; Marin Soljacic (CoRR, 2019)

Gated Orthogonal Recurrent Units: On Learning to Forget. - Li Jing 001; Çaglar Gülçehre; John Peurifoy; Yichen Shen 001; Max Tegmark; Marin Soljacic; Yoshua Bengio (Neural Comput., 2019)

Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications. - Rumen Dangovski; Li Jing 001; Preslav Nakov; Mico Tatalovic; Marin Soljacic (Trans. Assoc. Comput. Linguistics, 2019)

On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning. - Guillem Ramírez; Rumen Dangovski; Preslav Nakov; Marin Soljacic (CoRR, 2020)

Contextualizing Enhances Gradient Based Meta Learning. - Evan Vogelbaum; Rumen Dangovski; Li Jing 001; Marin Soljacic (CoRR, 2020)

Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs. - Allan dos Santos Costa; Rumen Dangovski; Samuel Kim; Pawan Goyal 004; Marin Soljacic; Joseph Jacobson (CoRR, 2020)

We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale. - Rumen Dangovski; Michelle Shen; Dawson Byrd; Li Jing 001; Desislava Tsvetkova; Preslav Nakov; Marin Soljacic (AAAI, 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 001 (IEEE BigData, 2021)

Non-Abelian gauge fields with fiber optics and beyond. - Yi Yang; Marin Soljacic (OFC, 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)

Discovering Sparse Interpretable Dynamics from Partial Observations. - Peter Y. Lu; Joan Ariño; 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)

Equivariant Contrastive Learning. - Rumen Dangovski; Li Jing 001; Charlotte Loh; Seungwook Han; Akash Srivastava; Brian Cheung; Pulkit Agrawal 001; Marin Soljacic (CoRR, 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)

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. - Samuel Kim; Peter Y. Lu; Srijon Mukherjee; Michael Gilbert; Li Jing 001; Vladimir Ceperic; Marin Soljacic (IEEE Trans. Neural Networks Learn. Syst., 2021)

Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. - Rumen Dangovski; Li Jing 001; Charlotte Loh; Seungwook Han; Akash Srivastava; Brian Cheung; Pulkit Agrawal 001; Marin Soljacic (ICLR, 2022)

DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings. - Yung-Sung Chuang; Rumen Dangovski; Hongyin Luo; Yang Zhang 001; Shiyu Chang; Marin Soljacic; Shang-Wen Li 001; Scott Yih; Yoon Kim; James R. Glass (NAACL-HLT, 2022)

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials. - Andrew Ma; Yang Zhang 001; Thomas Christensen; Hoi Chun Po; Li Jing 001; Liang Fu; Marin Soljacic (CoRR, 2022)

Discovering Conservation Laws using Optimal Transport and Manifold Learning. - Peter Y. Lu; Rumen Dangovski; Marin Soljacic (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)

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)

Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning. - Di Luo; Jiayu Shen; Rumen Dangovski; Marin Soljacic (CoRR, 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)

Asymmetric Grouped Convolutions for Logarithmic Scale Efficient Convolutional Neural Networks. - Li Jing 001; Rumen Dangovski; Marin Soljacic (HPEC, 2023)

Manifold Transfer Networks for Lens Distortion Rectification. - Li Jing 001; Lay Jain; Rumen Dangovski; Marin Soljacic (HPEC, 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 (HPEC, 2023)

Contextualizing Enhances Gradient Based Meta Learning for Few Shot Image Classification. - Evan Vogelbaum; Rumen Dangovski; Li Jing 001; Marin Soljacic (HPEC, 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 (ICML, 2023)

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. - Charlotte Loh; Seungwook Han; Shivchander Sudalairaj; Rumen Dangovski; Kai Xu 016; Florian Wenzel; Marin Soljacic; Akash Srivastava (ICML, 2023)

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

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

Geometry of contact: contact planning for multi-legged robots via spin models duality. - Baxi Chong; Di Luo; Tianyu Wang 010; Gabriel B. Margolis; Juntao He; Pulkit Agrawal 001; Marin Soljacic; Daniel I. Goldman (CoRR, 2023)

Model Stitching: Looking For Functional Similarity Between Representations. - Adriano Hernandez; Rumen Dangovski; Peter Y. Lu; 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)

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)

Learning to Optimize Quasi-Newton Methods. - Isaac Liao; Rumen Dangovski; Jakob Nicolaus Foerster; Marin Soljacic (Trans. Mach. Learn. Res., 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)

Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks. - Ileana Rugina; Rumen Dangovski; Li Jing 001; Preslav Nakov; Marin Soljacic (LREC/COLING, 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 (ICML, 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 (NeurIPS, 2024)

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

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)

Deep Learning and Symbolic Regression for Discovering Parametric Equations. - Michael Zhang; Samuel Kim; Peter Y. Lu; Marin Soljacic (IEEE Trans. Neural Networks Learn. Syst., 2024)

KAN: Kolmogorov-Arnold Networks. - Ziming Liu; Yixuan Wang; Sachin Vaidya; Fabian Ruehle; James Halverson; Marin Soljacic; Thomas Y. Hou 001; Max Tegmark (ICLR, 2025)

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics. - Andrew Ma; Owen Dugan; Marin Soljacic (CoRR, 2025)

L2M: Mutual Information Scaling Law for Long-Context Language Modeling. - Zhuo Chen; Oriol Mayné i Comas; Zhuotao Jin; Di Luo; Marin Soljacic (CoRR, 2025)

Learning simple heuristic rules for classifying materials based on chemical composition. - Andrew Ma; Marin Soljacic (CoRR, 2025)

AI-Driven Robotics for Free-Space Optics. - Shiekh Zia Uddin; Sachin Vaidya; Shrish Choudhary; Zhuo Chen; Raafat K. Salib; Luke Huang; Dirk R. Englund; Marin Soljacic (CoRR, 2025)