Christopher J. Earls

34 papers A* 4Journal 30

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Jacobian Scopes: token-level causal attributions in LLMs. CoRR Toni J. B. Liu, Baran Zadeoglu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
2025	J	`jnl`	Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture. Trans. Mach. Learn. Res. Thomas F. Burns, Tomoki Fukai, Christopher J. Earls
2025	A*	`conf`	Density estimation with LLMs: a geometric investigation of in-context learning trajectories. ICLR Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
2025	A*	`conf`	Lines of Thought in Large Language Models. ICLR Raphaël Sarfati, Toni J. B. Liu, Nicolas Boullé, Christopher J. Earls
2025	J	`jnl`	Text-Trained LLMs Can Zero-Shot Extrapolate PDE Dynamics. CoRR Jiajun Bao, Nicolas Boullé, Toni J. B. Liu, Raphaël Sarfati, Christopher J. Earls
2025	A*	`conf`	What's in a prompt? Language models encode literary style in prompt embeddings. EMNLP Raphaël Sarfati, Haley Moller, Toni J. B. Liu, Nicolas Boullé, Christopher J. Earls
2025	J	`jnl`	What's in a prompt? Language models encode literary style in prompt embeddings. CoRR Raphaël Sarfati, Haley Moller, Toni J. B. Liu, Nicolas Boullé, Christopher J. Earls
2025	J	`jnl`	chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data. CoRR Harshwardhan Praveen, Jacob Brown, Christopher J. Earls
2025	J	`jnl`	chebgreen: Learning and interpolating continuous Empirical Green's Functions from data. Comput. Phys. Commun. Harshwardhan Praveen, Jacob Brown, Christopher J. Earls
2024	J	`jnl`	Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture. CoRR Thomas F. Burns, Tomoki Fukai, Christopher J. Earls
2024	J	`jnl`	Density estimation with LLMs: a geometric investigation of in-context learning trajectories. CoRR Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
2024	A*	`conf`	LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law. EMNLP Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
2024	J	`jnl`	LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law. CoRR Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
2024	J	`jnl`	Lines of Thought in Large Language Models. CoRR Raphaël Sarfati, Toni J. B. Liu, Nicolas Boullé, Christopher J. Earls
2024	J	`jnl`	PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data. Neural Networks Robert Stephany, Christopher J. Earls
2024	J	`jnl`	Weak-PDE-LEARN: A weak form based approach to discovering PDEs from noisy, limited data. J. Comput. Phys. Robert Stephany, Christopher J. Earls
2023	J	`jnl`	Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data. CoRR Robert Stephany, Christopher J. Earls
2022	J	`jnl`	Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data. J. Comput. Phys. X Christophe Bonneville, Christopher J. Earls
2022	J	`jnl`	PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data. CoRR Robert Stephany, Christopher J. Earls
2022	J	`jnl`	PDE-READ: Human-readable partial differential equation discovery using deep learning. Neural Networks Robert Stephany, Christopher J. Earls
2022	J	`jnl`	Principled interpolation of Green's functions learned from data. CoRR Harshwardhan Praveen, Nicolas Boullé, Christopher J. Earls
2021	J	`jnl`	A new engineering theory describing oblique free surface impact by flexible plates. CoRR Wensi Wu, Christopher J. Earls
2021	J	`jnl`	Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data. CoRR Christophe Bonneville, Christopher J. Earls
2021	J	`jnl`	CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method. SoftwareX John Taylor Chavis III, Amy L. Cochran, Christopher J. Earls
2021	J	`jnl`	Data-driven discovery of physical laws with human-understandable deep learning. CoRR Nicolas Boullé, Christopher J. Earls, Alex Townsend
2021	J	`jnl`	Deep learning for classifying and characterizing atmospheric ducting within the maritime setting. Comput. Geosci. Hilarie Sit, Christopher J. Earls
2021	J	`jnl`	Gaussian processes for shock test emulation. Reliab. Eng. Syst. Saf. Christophe Bonneville, Maxwell Jenquin, Juan Londono, Alex Kelly, Jeffrey Cipolla, Christopher J. Earls
2021	J	`jnl`	PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning. CoRR Robert Stephany, Christopher J. Earls
2020	J	`jnl`	A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations. CoRR Wensi Wu, Christophe Bonneville, Christopher J. Earls
2020	J	`jnl`	CU-BENs: A structural modeling finite element library. SoftwareX Wensi Wu, Justyna Kosianka, Heather M. Reed, Christopher Stull, Christopher J. Earls
2019	J	`jnl`	A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer. IEEE Trans. Geosci. Remote. Sens. Marc Aurèle Gilles, Christopher J. Earls, David Bindel
2019	J	`jnl`	Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. CoRR Andrew E. Loeb, Christopher J. Earls
2019	J	`jnl`	Analysis of heterogeneous computing approaches to simulating heat transfer in heterogeneous material. J. Parallel Distributed Comput. Andrew E. Loeb, Christopher J. Earls
2016	J	`jnl`	Inverting for Maritime Environments Using Proper Orthogonal Bases From Sparsely Sampled Electromagnetic Propagation Data. IEEE Trans. Geosci. Remote. Sens. Vasileios Fountoulakis, Christopher J. Earls