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NeurIPS 2026SpotlightNational University of Singapore Deep learning theory

Point-to-Manifold Geometry: Flexibly Overcoming the Curse of Dimensionality in Neural Computational Units

Rohan Ghosh, Mehul Motani

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Indian Institute of Technology D Indian Institute of Technology R Deep learning theory

Statistical Mixing Guarantees for Contractive Echo State Networks

Pradeep Singh, Balasubramanian Raman

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Indian Institute of Technology D Indian Institute of Information Indian Institute of Technology R Deep learning theory

Dimension Bounds for Contractive Reservoir Computing from Input Entropy

Pradeep Singh, Kishore Babu Nampalle, Balasubramanian Raman

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harvard Deep learning theory

Temporal Smoothness Constraints on Efficient Neurobiological Codes Imply Temporal Specialization

John Vastola, Samuel J Gershman

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Hong Kong The Univeristy of Hongkong Deep learning theory

Looped Transformers with Layer Normalization Provably Learn the Power Method

Lyumin Wu, Chenyang Zhang, Yuan Cao

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Queensland U Melbourne U California, Berkeley UC Berkeley Deep learning theory

Why Heavy-Tailed Weights Predict Model Quality

Joseph Wilson, Chris van der Heide, Liam Hodgkinson, Zhichao Wang and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Peking Kuaishou- 快手科技 Kling, Kuaishou Kuaishou Technology Deep learning theory

On the Instability and Stabilization of Blockwise Muon

Yuanshi Liu, Weicheng Lin, Boyuan Jiang, Xin Tao and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, San Diego U California, Berkeley Deep learning theory

NTK Regression under dual power-law model: Deterministic Equivalents via SDE and PDE Methods

Collin Cranston, Zhichao Wang, Todd Kemp

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026State University of New York at U Florida University at Buffalo-SUNY State University of New York Buf Deep learning theory

Elastic Representations via Hyperbolic Geometry

Arjun Ramesh Kaushik, Rudrasis Chakraborty, Nalini Ratha, Venu Govindaraju

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The Hong Kong University of Scie The Hong Kong University of Scie Deep learning theory

The Alternation Depth Principle for Neural Operator Design

Haoze Song, Zhilu Lai, Wei Wang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Michigan Massachusetts Institute of Techn Massachusetts Institute of Techn Deep learning theory

Topological Invariance and Breakdown in Learning Dynamics

Yongyi Yang, Tomaso Poggio, Isaac Chuang, Liu Ziyin

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Santa Barbara Johns Hopkins Deep learning theory

Synaptic Strength Controls Trainability and Structural Stability in Rank-Deficient RNNs

Fatih Dinc, Edouard Ponnat, Henrik Weyer, Yanin Guerra and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Chinese University of Hong K The Chinese University of Hong K Deep learning theory

Spectral Degeneration of Softmax Attention under Isotropic Score Geometry

Mengda Li, Jianfeng Yao

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightU Electronic Science and Technol Deep learning theory

Phase Kernel Lifts Capacity of Dense Associative Memory

Yifei Zhao, Ying Tang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026B0Labs, N2TP Technology Nanyang Technological N2TP N2TP Technology Solutions JSC Hanoi University of Pharmacy Deep learning theory

Spectral Identifiability for World Models: Polynomial Projectors, Resolvent Stability, and a Krylov Bottleneck

Phan Quoc Hung Mai, Duc H Nguyen, Luong Doan, Ngoc Mai Vu and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harvard Deep learning theory

The Cost of Symmetry: Universality and Hardness for Permutation-Invariant Neural Networks

Dashiell Bhattacharyya

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026MIT Carnegie Mellon Deep learning theory

Fast Approximate $\ell_p$ Chamfer Distance via Lopsided Embeddings and Structured JL

Ying Feng, David Woodruff

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Academia Sinica Deep learning theory

Compositional Generalization Certificates via the Van Kampen Theorem

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026OralIndependent Zhejiang Deep learning theory

FLIP: Fast and Accurate Global Lipschitz Estimation for Large Feedforward Networks

Hongbo Chen, Zihao Ren

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hamburg University of Technology Ruhr University Bochum, ELLIS Un Ruhr University Bochum Technische Universität Hamburg Deep learning theory

The Optimal Control Foundation of Early Exits – Turnpikes and ResNets

Jens Püttschneider, Simon Heilig, Asja Fischer, Timm Faulwasser

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Kyoto Deep learning theory

Second-Order Complexity Theory for Neural Networks

Yoshihiro Maruyama

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ZHAW - Zürcher Hochschule für An Zurich University of Applied Sci Deep learning theory

A Reproducible Evaluation Protocol for Quantum Non-Linearity in Variational Quantum Models

Pavel Sulimov, Claude Lehmann

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Tsinghua Great Bay Shanghai Jiaotong Northwestern New York University, Shanghai Deep learning theory

Cauchy Scientific Networks: Loss–Architecture Alignment and Its Limit

Haonan Tan, Xin Li, juyi peng, Zhihong Xia and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Technical University of Munich TU Munich Deep learning theory

Spectral Re-Basin for Linear Mode Connectivity

Ya-Wei Eileen Lin, Thomas Dagès, Daniel Herbst, Daniel Cremers and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SpotlightKyoto Deep learning theory

Foundations of Categorical Equivariant Deep Learning

Yoshihiro Maruyama

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Kyoto Deep learning theory

Second-Order Complexity of Neural ODE Inference

Yoshihiro Maruyama

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Carnegie Mellon U Delaware CMU, Carnegie Mellon Deep learning theory

Beyond Eigenfunctions: Divergence Principal Functions for Representation Learning

Ritabrata Ray, Sahil Dharod, Burak Varıcı, Nicholas Boffi and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tsinghua Department of Biomedical Enginee Peking Qiyuan Lab Deep learning theory

A Hebbian Recurrent Neural Network Explains the Hierarchical Geometry of Sequence Memory

Zhitao FENG, Bo Ho, Huan Luo, xiaolong zou and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightDuke Duke Deep learning theory

What the Geometry of Good Models Tells Us

Alexis Fox, Samuel Orellana Mateo, Krish Yadav, Yiyang Sun and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026University College London, Unive Gatsby Unit, UCL Stanford Deep learning theory

How are linear representations learned? Exact solutions to the dynamics of abstraction

William W Yang, Peter E Latham, Andrew Saxe

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Qualcomm Datacenter Products Qualcomm Inc Qualcomm Deep learning theory

Phase Space Attention: A Hairer Lift Resolves the Single-Layer Induction Obstruction

Kingsuk Maitra, Shagun Sood, Morteza Hosseini, Suman Gunnala and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Technion - Israel Institute of T Technion - Israel Institute of T Amazon Technion - Israel Institute of T Deep learning theory

Surjective Pseudo-Invertible Neural Networks

Yamit Ehrlich, Amit Arad, Nimrod Berman, Assaf Shocher

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Lomonosov Moscow State Deep learning theory

Information bottleneck dynamics during learning across artificial and biological neural systems

Nikita Pospelov, Olga Ivashkina, Plusnin Viktor, Olga Rogozhnikova and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Electro-Commun Deep learning theory

Canonical Predictive Quotients: A Theory of Prediction under Hidden Predictive State Uncertainty with ICL Implications

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026AFRL Deep learning theory

Local-Interaction Learning Dynamics: A Markov Random Field Framework for Convergence of Deep Neural Network Learning

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightU North Carolina at Chapel Hill U North Carolina, Chapel Hill Deep learning theory

What Makes a Good Path? Factoring Manifold Support and Path Geometry

Zhixuan Zhou, Tingting Dan, Guorong Wu

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Case Western Reserve U North Carolina at Chapel Hill Deep learning theory

Emergence, Retention and Mitigation of Ill-conditioning due to Basis Lifting in KANs

Ferhat Arslan, Weihong Guo, Shuo Li

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Massachusetts Institute of Techn Deep learning theory

Understanding Double Descent through Universal Compression

Jiaxuan Cheng, Addison Spiegel

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026City University of Hong Kong Deep learning theory

Neural Spectral Capacity: An Architectural Quantity from Network Specification Alone

CHENYU ZHU, Ruoyu Zhao, Zhichao Lu

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Toronto Metropolitan Deep learning theory

A Mahalanobis Margin \texorpdfstring{$\gamma_{\min}$}{gamma\_min} Bound on Task Confusion in Pretrained Class-Incremental Learning: From Infeasibility to Exponential Attenuation

Milad Khademi Nori, Guanghui Wang

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Chiba Deep learning theory

Learning Modular Addition with Auxiliary Modulus

Hanato Kikuchi, Ryosuke Masuya, Kazuhiko Kawamoto, Hiroshi Kera

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Copenhagen University Of Copenhagen The American University in Cairo Copenhagen Deep learning theory

Characterizing Learning in Deep Neural Networks using a Tractable Algorithmic Complexity Estimator

Pedram Bakhtiarifard, Sophia Natasha Wilson, Mahmoud H. A. Afifi, Jonathan Wenshøj and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Kent, University of Cambridge U Oxford Halmstad NANYANG TECHNOLOGICAL UNIVERSITY Deep learning theory

Memorization Is Folding: Topological Signatures of Noisy-Label Learning

Zhongtian Sun, Fan Mo, Prayag Tiwari, KELIN XIA

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026New York Cold Spring Harbor Laboratory Deep learning theory

A Control-Theoretic Approximation to Predictive Coding Dynamics

Ryan Fayyazi, Kyle Daruwalla, Mitra Javadzadeh

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Leibniz University Hanover Deep learning theory

Hard Attention Transformers and BSS-Machines

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026National University of Singapore U Southern California Deep learning theory

Generalized Laplacian in Spectral Seriation on Manifold Data

Ruizi Wu, Wanjie Wang, Jinchi Lv

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026École Polytechnique CNRS-LISN BTU Cottbus-Senftenberg Deep learning theory

From Approximation to Computation: Universal Power of Deep Narrow Networks at Constant Width

Olivier Bournez, Johanne Cohen, Adrian Wurm

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Mila / University of Montréal Mila/ UdeM Mila Mila - Université de Montréal Deep learning theory

CP-MLPs: A Tensor-Rank Theory of Tied and Untied MLP Blocks

Md Rifat Arefin, Farzaneh Heidari, Irina Rish, Guillaume Rabusseau

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Berkeley U Melbourne UC Berkeley Deep learning theory

Spectral Estimation with Deformed Decompression

Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Michael Mahoney

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, San Diego UCSD U California-San Diego Deep learning theory

Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability

Tongtong Liang, Esha Singh, Rahul Parhi, Alex Cloninger and 1 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightU California, San Diego UCSD Deep learning theory

Sharpness of Minima in Deep Matrix Factorization

Anıl Kamber, Rahul Parhi

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Florida State Deep learning theory

Neural Expansion: A Unified Mechanism for How Deep Neural Network Generalize

Chashi Mahiul Islam, Samuel Jacob Chacko, Mao Nishino, Canlin Zhang and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026MIT U Texas at Dallas U Virginia Deep learning theory

On Lipschitz Explosion in Deep Neural Networks with Normalization: Consequences for Optimization and Robustness

Ashkan Soleymani, Reyhaneh Hosseinpourkhoshkbari, Hadi Daneshmand, Patrick Jaillet

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Fudan Deep learning theory

A Theory of Spatial Continuous Attractors in Hopfield Energy Landscapes

Chong Li, Xiangyang Xue, Jianfeng Feng, Taiping Zeng

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sorbonne Université, LOCEAN, LJL Inria Deep learning theory

Unrolled gradients in disguise: bridging interpolation-based and Jacobian regularization for stable neural dynamics

Maya Janvier, Etienne Meunier

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 202642.com Deep learning theory

Pattern-Based Matrix Analysis Under Permutations

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Wisconsin-Madison U Wisconsin - Madison Korea NVIDIA UW-Madison Deep learning theory

Fast Sandwich Products in Clifford Algebra

Travis Pence, Daisuke Yamada, Jiaqi Mo, Chanyoung Moon and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Deep learning theory

Comparing Linear Regions in ReLU-Type Networks: Theory and Monte Carlo Methods

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026New York University, Abu Dhabi New York New York University Abu Dhabi NYU Shanghai Deep learning theory

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension

George Andriopoulos, Zixuan Dong, Bimarsha Adhikari, Keith Ross

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Flatiron Institute + NYU Genentech Massachusetts Institute of Techn New York Deep learning theory

How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning

A stopgrad regression principle characterizes stationary points of stopgrad objectives and proves convergence to true flow maps while halving training memory.

Mark Goldstein, Max Shen, Zichu Wang, Aahlad Manas Puli and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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