11 citations · 21 across the 11 of their papers we have counts for
14 papers
Learning the Geometry of Materials Space with Trained Neural Tangent Kernels
Daniel Kaplan, Gabriel Kotliar, Anirvan M Sengupta
Neural networks trained to predict materials properties learn representations whose physical content can be difficult to extract. We use the neural tangent kernel (NTK), evaluated…
Flexible Online Representation Learning Based on Similarity Matching
Shagesh Sridharan, Yanis Bahroun, Anirvan M. Sengupta
Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data. Such a representation can deal with the dense connect…
Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics
Matthew Smart, Soumya Ganguly, Nilava Metya +2
We study minimal attention-only transformers under all-token corruption and show they admit a two-stage empirical Bayes interpretation. A single attention step computes a kernel-we…
Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
Luciano Loris Viteritti, Riccardo Rende, Christopher Roth +3
The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its…
A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation
Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin +4
We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window of its input history onto a ca…
Reading Qubits with Sequential Weak Measurements: Limits of Information Extraction
Cesar Lema, Aleix Bou-Comas, Atithi Acharya +2
Quantum information processing and computation requires high accuracy qubit configuration readout. In many practical schemes, the initial qubit configuration has to be inferred fro…
Neurons as Detectors of Coherent Sets in Sensory Dynamics
Joshua L. Pughe-Sanford, Xuehao Ding, Jason J. Moore +4
We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact representations…
Double descent: When do neural quantum states generalize?
M. Schuyler Moss, Alev Orfi, Christopher Roth +5
Neural quantum states (NQS) provide flexible and compact wavefunction parameterizations for numerical studies of quantum many-body physics. In particular, NQS aim to circumvent the…
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval
Matthew Smart, Alberto Bietti, Anirvan M. Sengupta
We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfie…
Deep Learning Based Superconductivity: Prediction and Experimental Tests
Daniel Kaplan, Adam Zhang, Joanna Blawat +6
The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and comput…
Learning interactions between Rydberg atoms
Olivier Simard, Anna Dawid, Joseph Tindall +3
Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To…
Fast Scrambling at the Boundary
Ancel Larzul, Anirvan M. Sengupta, Antoine Georges +1
Many-body systems which saturate the quantum bound on chaos are attracting interest across a wide range of fields. Notable examples include the Sachdev-Ye-Kitaev model and its vari…
Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
Jiawei Zang, Matija Medvidović, Dominik Kiese +3
Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization g…
Compressing the two-particle Green's function using wavelets: Theory and application to the Hubbard atom
Emin Moghadas, Nikolaus Dräger, Alessandro Toschi +7
Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly exciti…