We try to include links for all of our recent papers. Most links direct you to a journal’s site where that particular publication is available for download. If you cannot access one of our papers, let us know. For a more complete list, see Michael’s google scholar citations profile.


Beyond Linear Response: Equivalence between Thermodynamic Geometry and Optimal Transport
Adrianne Zhong and Michael DeWeese

More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
James Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin
[ICLR ‘24] [arXiv]

The lazy (NTK) and rich (μP) regimes: a gentle tutorial
Dhruva Karkada


The eigenlearning framework: A conservation law perspective on kernel ridge regression and wide neural networks
James Simon, Madeline Dickens, Dhruva Karkada, Michael DeWeese
[TMLR ‘23] [arXiv] [code]

Shortcut engineering of active matter: run-and-tumble particles
Adam Frim and Michael DeWeese

Time-Asymmetric Fluctuation Theorem and Efficient Free Energy Estimation
Adrianne Zhong, Benjamin Kuznets-Speck, Michael DeWeese

A Spectral Condition for Feature Learning
Greg Yang, James Simon, Jeremy Bernstein

On the Stepwise Nature of Self-Supervised Learning
James Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham Fetterman, Joshua Albrecht


Reverse Engineering the Neural Tangent Kernel
James Simon, Sajant Anand, Michael DeWeese
[ICML ‘22] [arXiv] [code]

Geometric Bound on the Efficiency of Irreversible Thermodynamic Cycles
Adam Frim and Michael DeWeese
[PRL ‘22]

Optimal Finite-Time Brownian Carnot Engine
Adam Frim and Michael DeWeese
[PRE ‘22]

Limited-control optimal protocols arbitrarily far from equilibrium
Adrianne Zhong and Michael DeWeese

A Solution to the Fokker-Planck Equation for Slowly Driven Brownian Motion: Emergent Geometry and a Formula for the Corresponding Thermodynamic Metric
Neha Wadia, Ryan Zarcone, Michael DeWeese

Sparse coding models predict a spectral bias in the development of primary visual cortex (V1) receptive fields
Andrew Ligeralde and Michael DeWeese


Engineered Swift Equilibration for Arbitrary Geometries
Adam Frim, Adrianne Zhong, Stephen Chen, Dibyendu Mandal, Michael R DeWeese
[PRE] [arXiv]

Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Charles Frye, Jamie Simon, Neha Wadia, Andrew Ligeralde, Michael DeWeese, Kris Bouchard
[Neural Computation]


Efficient sensory coding of multidimensional stimuli
Thomas Yerxa, Eric Kee, Michael DeWeese, Emily Cooper
[PLOS Computational Biology]

Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise
Pratik Sachdeva, Jesse Livezy, Michael DeWeese
[Neural Computation]


Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons
Michele Insanally, Ioana Carcea, Rachel Field, Chris Rodgers, Brian DePasquale, Kanaka Rajan, Michael DeWeese, Badr Albanna, Robert Froemke

On the sparse structure of natural sounds and natural images: similarities, differences, and implications for neural coding
Eric Dodds, Michael DeWeese
[Frontiers in Computational Neuroscience]

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network
Louis Kang, Michael DeWeese

Design of optical neural networks with component imprecisions
Michael Fang, Sasikanth Manipatruni, Casimir Wierzynski, Amir Khosrowshahi, Michael DeWeese
[Optics Express]

Numerically recovering the critical points of a deep linear autoencoder
Charles Frye, Neha Wadia, Michael DeWeese, Kris Bouchard

Spatial whitening in the retina may be necessary for V1 to learn a sparse representation of natural scenes
Eric Dodds, Jesse Livezey, Michael DeWeese