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NeurIPS 2026Representation & contrastive learning

Structure-agnostic Causal Representation Learning

SaCRL jointly identifies causal structure and learns invariant representations via soft optimization over HSIC-based invariance violations without prior structural knowledge. It guarantees structure identification, invariance satisfaction, and out-of-distribution generalization while achieving state

Arman Behnam, Binghui Wang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published Oct 1, 2026 · 0 citations

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arXivChinese University of Hong Kong Shanghai Artificial Intelligence Representation & contrastive learning

MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception

MinkowskiPE applies Lorentz transformations via joint spacetime positional encoding to make attention depend only on relative displacement, improving molecular dynamics and video prediction with far fewer parameters.

Yuhao Li, Louie Hong Yao, Tianyi Shi, Hanqun Cao and 3 more

Published Sep 27, 2026 · 0 citations · ▲ 14 on Hugging Face

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arXivRepresentation & contrastive learning

JEPA-Anything: Learning Predictive Models across Different Worlds

JEPA-Anything uses orthogonal predictive factorization to learn cross-domain predictive models that outperform baselines in vision, biology, clinical, control, molecular, physical, and weather domains.

Taoyong Cui, Zhongyao Wang, Xinyue Xu, Weiyang Liu and 9 more

Published Sep 17, 2026 · 0 citations · ▲ 77 on Hugging Face · Code ★ 273

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NeurIPS 2026U Illinois Urbana-Champaign Max Planck Institute for Mathema SolverX Representation & contrastive learning

SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning

Jongann Lee, Sun Woo Park, Yun Young Choi

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

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NeurIPS 2026Massachusetts Institute of Techn MIT UCSF Representation & contrastive learning

The Optimization Prior: Instilling depth for shallow networks, detail for coarse networks

Vighnesh Subramaniam, Boris Katz, Brian Cheung

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

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NeurIPS 2026Google U Southern California Google Research UC San Diego / Mila Representation & contrastive learning

A Foundational Model System for Datacenter Machine Repairs

Yuanlin Wen, Elan S Markowitz, Zubo Gu, Sami Abu-El-Haija and 10 more

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

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NeurIPS 2026U Surrey Representation & contrastive learning

Timing Is All You Need: SpikeCore, Learnable Delays, and Gain Control for Neuromorphic Classification

Mohsen Kamelian Rad, Sotiris Moschoyiannis, Roman Bauer

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

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NeurIPS 2026Northwest Polytechnical Universi Xi'an Jiaotong Representation & contrastive learning

CASAM: Consistency-Anchored Sharpness-Aware Minimization for Improved Model Generalization

Chengli Tan, Tianyu Wang, Junmin Liu, Yong Xu

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

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NeurIPS 2026Université de Liège U Liège U Liege Representation & contrastive learning

Neuromodulated Constrained Autoencoders for Context-Dependent Manifold Learning

Jérôme Adriaens, Gustave Bainier, Guillaume Drion, Pierre Sacré

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

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NeurIPS 2026U Science and Technology of Chin Huazhong University of Science a Institute of automation, Chinese Representation & contrastive learning

MemTailor: Hierarchical Memory-Augmented Multi-Expert Learning for Long-Tailed Recognition

Yuyang Sun, Senyang Su, Xiaotian Wang, Pengkun Wang and 1 more

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

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NeurIPS 2026U Toronto Broad Institute U Toronto & Vector Institute U Toronto/Vector Institute/SickK Representation & contrastive learning

DynaSub: Adaptive Subgrouping for Scalable Representation Learning

Tina Behrouzi, Sana Tonekaboni, Rahul Krishnan, Anna Goldenberg

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

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NeurIPS 2026Offenburg Institute for Machine Learning a Representation & contrastive learning

2D Spatial Reasoning with Adaptive Neural Cellular Automata

Martin Spitznagel, Janis Keuper

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Griffith City University of Hong Kong Representation & contrastive learning

Permutation Sensitivity in t-SVD-based Multi-view Clustering

Jintian Ji, Yanjun Zhang, He Zhang, Leo Yu Zhang 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 2026Jinan Representation & contrastive learning

Learning Event-to-Field Operators Without Interpolation

xingyu sha

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

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NeurIPS 2026Bosch Robert Bosch Research in North A Representation & contrastive learning

Balanced Multi-Task Learning from an Optimality-Gap Perspective

Ce Liu, Jianing Huang, HUANG Sicheng, Shu Liu 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 2026California State University, Nor U Trento MPI for Intelligent Systems, Tüb Representation & contrastive learning

PVFormer: Proper Velocity Transformer for Stable and Scalable Hyperbolic Representation Learning

Xianglong Shi, Nicu Sebe, Bernhard Schölkopf, Ziheng Chen

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

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NeurIPS 2026Donghua Donghua University, Shanghai Representation & contrastive learning

Decentralized Coupled Representation Learning

Zilin Li, Weiwei Xu, Xuchun Tong, Xuanbo Lu and 2 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 Science and Technology of Chin Representation & contrastive learning

Distributed-Order Fractional Spiking Neural Network

Chengjie Ge, Yufeng Peng, Qiyu Kang, Xueyang Fu 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 2026Texas A&M Texas A&M Lambda, Inc Lambda Representation & contrastive learning

Spin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)-Equivariant Networks

Chenxing Liang, Yuchao Lin, Andrii Kryvenko, Wendi Yu and 4 more

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

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NeurIPS 2026U Cambridge Representation & contrastive learning

Robust Amortized Simulation-Based Inference via Learned Error Models

Matthew O&#x27;Callaghan, Kaisey Mandel, Gerard Gilmore

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

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NeurIPS 2026Southeast Zhengzhou Representation & contrastive learning

Individuals Matter: Improving Deep Multi-View Clustering via Explicit Single-View Enhancement

Hanyang Li, Yanzheng Wang, Yaxin Hou, Zhengxing Jiao 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 2026Jinan Dalian University of Technology Representation & contrastive learning

pyCD: A Unified Benchmark for Reliable Evaluation of Cognitive Diagnosis Models

Youheng Bai, Xueyi Li, Tengteng Cheng, Mingliang Hou and 3 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 Sheffield Representation & contrastive learning

Less Structure is More: Minimal Representations for Supervised Learning

Menghui Zhou, Vitaveska Lanfranchi, Po Yang

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

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NeurIPS 2026CWI Universität zu Lübeck Representation & contrastive learning

SpikingGamma: Temporally Precise Online SNN Training Through Smoothed Temporal Delays

Roel Koopman, Sebastian Otte, Sander Bohte

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

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NeurIPS 2026KU Leuven Representation & contrastive learning

$\mathcal{P}$Torch: Narrowing the Gap Between Projection and Gradient-Based Learning

Jeff Cyuzuzo Jambé, Jan Quan, Panagiotis Patrinos

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

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NeurIPS 2026Nanjing University of Science an Representation & contrastive learning

Memory-Driven Contrastive Embedding Enhancement for Fine-Grained Open-Set Semi-Supervised Learning

Yinan Han, Qing-Yuan Jiang

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

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NeurIPS 2026Victoria University of Wellingto Representation & contrastive learning

Deadline-Constrained Dynamic Workflow Scheduling Can be Cast as a Representation Learning Problem

Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang

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

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NeurIPS 2026Independent Researcher Representation & contrastive learning

Layer Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant

Amirhossein Yousefiramandi

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026McGovern Institute for Brain Res Ritsumeikan Representation & contrastive learning

Loop alignment: Self-organized Weight Transpose in Predictive Coding through Independent Hebbian Plasticity.

Kojiro Hirokane, Ryohei Ueno, Takashi Kitsukawa

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Beijing Normal University, Schoo Beijing Normal Representation & contrastive learning

Gram-Calibrated Anchoring for Class-Incremental Learning

Qi Zhu, Ziang Gan, Libao Zhang

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

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NeurIPS 2026ServiceNow Inc ServiceNow IIIT Hyderabad Mila / Université de Montréal Montreal Institute for Learning Representation & contrastive learning

Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

Jishnu S Nair, Patrice Bechard, Rishabh Maheshwary, SRAVAN RAMACHANDRAN and 13 more

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

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NeurIPS 2026China University of Mining and T China University of Mining Techn Representation & contrastive learning

Beyond the Readout: Reservoir State Statistics for Model-Space Learning under Sparse Observations

Ao Chen, Yijing Lu, Pengpeng Chen

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

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NeurIPS 2026Guangdong Provincial Laboratory Chinese Medicine Guangdong Labor Macau University of Science and Department of Computer Science a Representation & contrastive learning

Self-Supervised Reconstruction Knockoffs for Calibrated Unsupervised Feature Selection

HaiHui Huang, Dingkui Kang, Yanan Zhou, Yong Liang

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

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NeurIPS 2026Sony AI Vrije Universiteit Amsterdam Fondazione Bruno Kessler U Edinburgh Representation & contrastive learning

Where to Approximate in Neurosymbolic Inference?

Samy Badreddine, Emile van Krieken, Luciano Serafini, Antonio Vergari

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Stanford Percepta Cornell Representation & contrastive learning

Refactoring Code Through Library Design

Žiga Kovačič, Justin Chiu, Celine Lee, Wenting Zhao and 1 more

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

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NeurIPS 2026Massachusetts Institute of Techn Michigan State MIT-IBM Watson AI Lab Representation & contrastive learning

Dynamic Convolutions Improve Transformers

Oliver Sieberling, Bharat Runwal, Rameswar Panda, Yoon Kim

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

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NeurIPS 2026MIT Massachusetts Institute of Techn International Centre for Theoret Representation & contrastive learning

The Marauder’s Map: Bézier Manifolds Reveal Hidden Surfaces for Model Merging and Ensembling

Abhiram Iyer, Mark T Harnett, Sarthak Chandra

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

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NeurIPS 2026Gustave-Eiffel EPFL EPFL - EPF Lausanne Representation & contrastive learning

Recovering the Apresjan Hierarchy Using Linkage-Based Clustering

Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda, Patrick Thiran

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

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NeurIPS 2026U Sydney The University of Sydney Representation & contrastive learning

Approximation in Contrastive Representation Learning

Yuanfan Li, Zihan Zhang, Yiming Ying, Ding-Xuan Zhou

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

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NeurIPS 2026U Sydney Adelaide Shanghai Normal Shanxi University Of Technology Sydney Representation & contrastive learning

Resolution-Aware Structural Density Peak Clustering

Jie Yang, Hsiang-Ting Chen, Yan Ma, Xinyan Liang and 7 more

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

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NeurIPS 2026National Cheng Kung Academia Sinica Representation & contrastive learning

Distance-Dependent Connectivity Shapes Continual Learning by Synaptic-Resource-Delimited Separation of Neural Dynamics

CHIU-CHANG CHENG, Ching-Lung Hsu, Ya-Ning Chang, Chao-Hung Wang

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

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NeurIPS 2026Roku Representation & contrastive learning

k-th Order Deep Homomorphism Networks

Takanori Maehara

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

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NeurIPS 2026Hanyang Oxford Representation & contrastive learning

Saddle-to-Saddle Dynamics in Self-Supervised Shortcut Learning

Juhwan Kim, yoonsoo nam, Sungyoon Lee

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

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NeurIPS 2026SpotlightImperial College London Representation & contrastive learning

Casper: A Projection-Based Neurosymbolic Layer for Scalable & Guaranteed Constraint Satisfaction

Lohith Konathala, Luca Andolfi, Eleonora Giunchiglia

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Electronic Science and Technol Tsinghua Fudan Representation & contrastive learning

SpikeSSL: A Universal Spike Inference Framework with Dynamics-Informed State-Space Layers

Chenghao Yue, Siming Xing, shuran liu, Angran Li and 1 more

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

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NeurIPS 2026Chongqing chongqing Representation & contrastive learning

Learning Joint Semantic-Geometric Uncertainty for Structured Prediction with Closed-Loop Calibration

Bohan Li, Rui Leng, Qi Ma, Shaoyuan Mo and 1 more

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

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NeurIPS 2026Toronto Metropolitan Representation & contrastive learning

The Structural Bias of $\ell_2$-Regularized Cross-Entropy Heads in Class-Incremental Learning: Characterization, Limits, and a Gauge-Anchoring Fix

Milad Khademi Nori, Guanghui Wang

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

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NeurIPS 2026U Cambridge Representation & contrastive learning

Scaling Causal Reasoning with Increasingly Complex Causal Simulators

Nicolás Astorga, Anita Kriz, Mihaela van der Schaar

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

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NeurIPS 2026KAIST Korea Advanced Institute of Scie Korea Advanced Institute of Scie Representation & contrastive learning

BabyTheorist: A Benchmark for Learning to Theorize the World from Observation Alone

Doojin Baek, Junyeob Baek, Mingyu Jo, Hosung Lee 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 2026U California, Berkeley Uni Tübingen / MPI-IS U California at Berkeley Technische Universität Darmstadt Representation & contrastive learning

What Is Worth Representing? Representational Empowerment for Continual Model Construction

Fei Dai, Hanqi Zhou, Alison Gopnik, Charley M Wu

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

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NeurIPS 2026Massachusetts Institute of Techn CMU Virginia Polytechnic Institute a Carnegie Mellon Representation & contrastive learning

Position: Neurosymbolic AI is a strong technical foundation for trustworthy, deployable AI by design

Chandler Squires, Yaqi Xie, Simon Stepputtis, Katia Sycara and 1 more

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

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NeurIPS 2026U Pennsylvania Representation & contrastive learning

Position: The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models

Adam Stein, Aaditya Naik, Neelay Velingker, Mayur Naik and 1 more

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

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NeurIPS 2026Queen's Queens Queen’s Representation & contrastive learning

A Theoretical Bridge Between Long-Tailed Recognition and Continual Learning

Mahdiyar Molahasani, Michael Greenspan, Ali Etemad

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

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NeurIPS 2026Tel Aviv Open University of Israel Representation & contrastive learning

Semantic Concept Steering Breaks the Explanation Drift Loop in Continual Learning

Yehonatan Elisha, Oren Barkan, Noam Koenigstein

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

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NeurIPS 2026Yonsei Representation & contrastive learning

Not All Routing Drift Is Harmful: Trust-Region Projection for Class-Incremental Learning

Tae-Hwan Kim, Sung-Bae Cho

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

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NeurIPS 2026Carnegie Mellon Representation & contrastive learning

Projection Learning: A Principled Way to Overcome Memorization in Distribution Learning

Lin Chen, Dejan Slepcev

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

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NeurIPS 2026Technion Technion / NVIDIA Research Representation & contrastive learning

Positional Encoding Is All You Need For Scalable Equivariance Constraint Relaxation

Hagay Michaeli, Haggai Maron, Daniel Soudry

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

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NeurIPS 2026Institute of Theoretical and App AGH University of Science and Te American U Rochester Representation & contrastive learning

Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning

Katarzyna Filus, Kamil Faber, Roberto Corizzo, Christopher Kanan

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

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NeurIPS 2026Korea Telecom Paris, IP Paris Korea Institute of Science and T Representation & contrastive learning

Rethinking Attention in Depth for Operator Learning

Jaehyeon Lee, Kiwon Um, JungHyun Han, Min-Koo Kang

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

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NeurIPS 2026Universität Bern U Bern U Bern and Heidelberg Department of Physiology, Univer Representation & contrastive learning

K-prop: Deep Online Learning by Backpropagating Temporal Kernels

Bingkun Liu, Paul Haider, Federico Benitez, Mihai A. Petrovici and 1 more

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

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NeurIPS 2026Beijing Normal Shandong University of Technolog Representation & contrastive learning

Filtered-Trace Online Variational Training for Probabilistic Spiking Neural Networks

Yaokun Wang, Tiantian Xiao, Hongyan Ding, Zhi Yan

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

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NeurIPS 2026ELLIS Institute Tübingen, Max Pl ETH Zurich; Max-Planck-Institute Max Planck Institute for Intelli ETHZ - ETH Zurich ETH Zurich Representation & contrastive learning

Fixed-Point Reasoning: Stable and Adaptive Deep Looped Models

Sajad Movahedi, Shlomo Libo Feigin, Vera Milovanović, Alexander Theus and 4 more

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

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NeurIPS 2026Rochester Institute of Technolog The University of Memphis Representation & contrastive learning

Class-Mixed Diffusion Augmentation for Shortcut-Breaking in Continual Learning

Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Sydney U Sydney, University of Sydney HKBU / RIKEN Mohamed bin Zayed University of Representation & contrastive learning

Adaptive Multi-Frame Learning for Expressive and Stable Atomic Representations

Jun Wang, Yifan Zeng, Bo Han, Fengwang Li 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 2026Posts & Telecommunications I VNU University of Engineering an Vietnam National University, Han Representation & contrastive learning

Disentangling Optimization Geometry via Hierarchical Polar Adapters for Class-Incremental Learning

Dat Q Mac, Thanh Hai Dang, Duc-Trong Le, Quynh-Trang Pham Thi

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

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NeurIPS 2026Renmin University of China Shanghai Lixin University of Acc Representation & contrastive learning

RoLL: Robust Low-Rank Learning via Nesterov Momentum

Zhaojun Hu, Wenchen Liu, Ting Wei, Biao Mei and 1 more

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

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NeurIPS 2026Tsinghua University, Tsinghua Southwest Jiaotong Representation & contrastive learning

Beyond MNIST: Limitations of Amplitude Encoding on Quantum Classification

Xin Wang, Yabo Wang, Rebing Wu

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

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NeurIPS 2026Georgia Institute of Technology Tsinghua Fudan Tsinghua University, Tsinghua Representation & contrastive learning

Leveraging Dale’s Principle as an Inductive Bias in Recurrent Neural Networks

Jiyi Wang, Chenxiao Yang, Yujia Zhao, Jingzhao Zhang and 1 more

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

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NeurIPS 2026Texas Tech U Texas at El Paso Representation & contrastive learning

A Deep Learning Framework for Scalar-on-Function Models

Ruiqi Liu, Suneel Babu Chatla

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

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NeurIPS 2026U Vienna Webster Vienna Private Universität Vienna Representation & contrastive learning

HyperTree: Scalable Unsupervised Hierarchy Discovery in Hyperbolic Space

Thomas Lang, Kevin Sidak, Anna Beer, Sebastian Tschiatschek and 2 more

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

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NeurIPS 2026U St. Gallen Universität St. Gallen U Notre Dame National University of Singapore Weight Space Labs & Universi Representation & contrastive learning

Weight Space Learning needs to unify benchmarking! A taxonomy of evaluation practices

Tobias Ettling, Damian Falk, Aron Asefaw, Léo Meynent and 6 more

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

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NeurIPS 2026East China Normal Shanghai Jiaotong Shanghai Jiao Tong Representation & contrastive learning

MIRAGE: Duality-Inspired MILP Augmentation for Representation Learning

Ziao Guo, Shiyue Wang, Jiayuan Yang, Junchi Yan

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

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NeurIPS 2026Yerevan State YerevaNN, Yerevan State National Research Council, Italy Representation & contrastive learning

Scaling Laws for Synthetic Pretraining in Radio-Map Prediction

Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan, Hrant Khachatrian and 1 more

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

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NeurIPS 2026The Hong Kong University of Scie Tsinghua Institute for AI Industry Resear Representation & contrastive learning

LogicSR: A Unified Benchmark for Logical Discovery from Data

Zimeng Zhang, Xin Zheng, Feifei Zhang, Yunxin Liu and 1 more

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

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NeurIPS 2026Pontificia Universidade Catolica Pontifícia Universidade Católica Representation & contrastive learning

Hierarchical Agglomerative Clustering via Relaxed Representatives

Eduardo Laber, Miguel A Batista

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

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NeurIPS 2026UiT - The Arctic University of N U Tromsø UiT The Arctic University of Nor Université Bretagne Sud Norwegian Computing Center Representation & contrastive learning

NOFE – Neural Operator Function Embedding

NOFE introduces a continuous neural operator framework for function dimensionality reduction that outperforms PCA, t-SNE, and UMAP in local structure preservation and sampling-independent embeddings.

Lars Uebbing, Harald Lykke Joakimsen, Siyan Chen, Georgios Leontidis and 5 more

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

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NeurIPS 2026Friedrich-Alexander University o Friedrich-Alexander Universität Representation & contrastive learning

Rethinking Neural Nonlinearity as Gating

Standard neural activations are instances of input-conditioned threshold gating, enabling lossless pretrained conversion and hardware-efficient training.

MUHAMMAD SABIH, Frank Hannig, Jürgen Teich

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Kempner Institute at Harvard Representation & contrastive learning

Amortized Vine Copulas for High-Dimensional Density and Information Estimation

VDC amortizes vine-copula fitting via a reused bivariate denoising model with Sinkhorn projection, enabling faster high-dimensional density and mutual information estimation.

Houman Safaai

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

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NeurIPS 2026U Pennsylvania, University of Pe U Pennsylvania Representation & contrastive learning

Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration

InvLT calibrates uncertainties by applying a shared monotonic scalar MLP to logits, preserving predictions independent of class count and outperforming baselines on standard benchmarks.

Lening Zhao, Qipeng Zhan, Li Shen

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

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NeurIPS 2026U Texas at Austin Seoul National U Texas, Austin Representation & contrastive learning

Structured Masked Diffusion for Joint Multiuser Decoding

CIDER is a masked-diffusion multiuser decoder using demixing and parity-aware propagation to outperform joint belief propagation by 6-100x in speed with matching error rates.

Taekyun Lee, Jiyoung Yun, Jeffrey Andrews, Hyeji Kim

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

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NeurIPS 2026Vrije Universiteit Amsterdam Sony Computer Science Lab Leiden University Medical Center Representation & contrastive learning

Hierarchical Conformal Classification

Hierarchical conformal classification extends prediction sets to class hierarchies via constrained optimization, maintaining coverage guarantees while yielding smaller, semantically structured sets that annotators prefer.

Floris den Hengst, Inès Blin, Majid Mohammadi, Syed I Shah and 1 more

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

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NeurIPS 2026INRIA Advanced Machine Intelligence, I Representation & contrastive learning

PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment

PEIRA introduces a non-contrastive self-supervised objective via linear regressor traces whose only stable equilibria recover canonical correlation subspaces, matching VICReg and LeJEPA performance.

Michael Arbel, Basile Terver, Jean Ponce

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

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NeurIPS 2026Mohamed bin Zayed University of SeeViaAI MBZUAI Mohamed bin Zayed University of Skoltech Representation & contrastive learning

Convex Compositional Reasoning Models

Convex Compositional Energy Minimization uses input-convex factor networks and convex relaxation to enable scalable deterministic compositional reasoning that transfers to larger instances without retraining.

Meir Roketlishvili, Semen Semenov, Maksim Bobrin, Viktor Kovalchuk and 6 more

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

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NeurIPS 2026Nanyang Technological The Hong Kong University of Scie Tencent Representation & contrastive learning

Delve into the Applicability of Advanced Optimizers for Multi-Task Learning

Advanced optimizers weaken multi-task learning because instant gradients barely affect updates, so the APT framework with adaptive momentum and Muon direction preservation improves results across four datasets.

Zhipeng Zhou, Linxiao Cao, Yiming Cao, Pengcheng Wu 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 2026SpotlightTelepath The Sequel Institute New York NYU Representation & contrastive learning

Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

Prospective bottom-up inputs via two-tap updates mitigate depth-dependent gradient attenuation in deep continuous-time recurrent networks, boosting RQF accuracy on Speech Commands and Path-X.

Shivang Rawat, Mirko Morello, Flaviano Morone, David Heeger

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

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NeurIPS 2026Université Paris-Saclay CentraleSupelec Laboratoire de recherche en info Representation & contrastive learning

Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures

Linearizing JEPA gradient flow reveals competing drive and decay effects that unify collapse-avoidance heuristics and predict a stability phase boundary, leading to ResidualPred, which improves rank and accuracy.

José Lucas De Melo Costa, Seong Woo Ahn, Fabrice Popineau, Arpad Rimmel and 1 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 Science and Technology of Chin Anhui Representation & contrastive learning

LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning

LongSpike proposes fractional-order spiking state-space models that integrate long-memory kernels into spiking neural networks, outperforming state-of-the-art SNNs on long-sequence benchmarks while preserving sparse computation.

Xinrui He, Qiyu Kang, Xuhao Li, Zheng-Jun Zha

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

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NeurIPS 2026Imperial College London Representation & contrastive learning

Geometry-Constrained Kolmogorov–Arnold Networks: Learning Edge Geometry via Banach Duality

Geometry-constrained KANs learn per-edge Banach exponents via duality to adapt function-space geometry, matching fixed-basis baselines on symbolic regression while improving noise robustness and small-sample accuracy.

Senanayak Sesh Kumar Karri

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

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lenient 3/5

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NeurIPS 2026Imperial College London Google DeepMind, Mila Representation & contrastive learning

Overcoming Rank Collapse in Feedback Alignment

Feedback Alignment suffers rank collapse in deep networks, so orthogonal optimizers and activation normalization boost accuracy by up to 9 points by increasing effective gradient dimensionality.

Gauthier Boeshertz, Razvan Pascanu, Claudia Clopath

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

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NeurIPS 2026U Trento KU Leuven ETH Zurich Representation & contrastive learning

Divide et Calibra: Multiclass Local Calibration via Vector Quantization

Divide et Calibra uses vector quantization to learn shared, region-specific multiclass calibration maps that improve local calibration without reducing latent dimensions.

Cesare Barbera, Lorenzo Perini, Giovanni De Toni, Andrea Passerini and 1 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Max Planck Institute For Intelli MPI for Intelligent Systems, Tüb Max-Planck Institute Representation & contrastive learning

PENEX: AdaBoost-Inspired Neural Network Regularization

PENEX introduces a multi-class exponential loss optimized via first-order methods that increases margins and improves neural network generalization in low-data regimes.

Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach

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

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NeurIPS 2026SpotlightShanghai Jiaotong California Institute of Technolo Representation & contrastive learning

Winfree Oscillatory Neural Network

Winfree Oscillatory Neural Network applies generalized synchronization dynamics to vision and reasoning tasks, scaling to ImageNet-1K and achieving 80.1% Maze-hard accuracy with 1% of prior parameters.

Jiawen Dai, Yue Song

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

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NeurIPS 2026U Texas at Austin Representation & contrastive learning

Bayesian Additive Distribution Regression

DistBART applies BART priors to distribution regression via Riesz representers, yielding adaptive convergence, nonlinear extensions, and scalable random-feature inference.

Antonio R Linero, Jared Murray, Soumyabrata Bose

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

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NeurIPS 2026Seoul National UNIST Ewha Womans Representation & contrastive learning

A Composite Activation Function for Learning Stable Binary Representations

HTAF smoothly approximates Heaviside via a sigmoid-tanh composite to enable stable gradient-based training of binary neural networks, yielding interpretable ICBMs with comparable or superior accuracy.

Seokhun Park, Choeun Kim, Kwanho Lee, Sehyun Park and 2 more

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

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NeurIPS 2026U Notre Dame Representation & contrastive learning

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

CoDEAL integrates neural covariate adjustments with autoencoder factor structures for heterogeneous causal panel data and proves convergence of counterfactual estimates.

Guanhao Zhou, Yuefeng Han, Xiufan Yu

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

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NeurIPS 2026U Southern California Novateur Research Solutions U Central Florida Representation & contrastive learning

TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond

TraXion introduces MESES axioms and a pre-training framework for multi-entity spatiotemporal event streams that beats mobility baselines and generalizes to security and health logs.

Shang-Ling Hsu, Mark Tenzer, Cyrus Shahabi, Khurram Shafique

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Alberta Massachusetts Institute of Techn MIT U Alberta & Amii Representation & contrastive learning

Measure-to-measure Regression with Transformers

This work formalizes nonlinear measure-to-measure regression and introduces two scalable transformer-based approaches for learning operators between probability distributions. The methods generalize to unseen measures in synthetic experiments, particle systems, and a large-scale colorectal cancer or

Matthew Vandergrift, Martha White, Yury Polyanskiy, Philippe Rigollet 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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NeurIPS 2026Emory Representation & contrastive learning

Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning

Contrastive predictive objectives that sample negatives across trajectories confuse slowly varying noise with true dynamics, but intra-trajectory negative sampling removes this shortcut and improves learned dynamics representations.

Paarth Gulati, Ilya Nemenman

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Korea Korea university Representation & contrastive learning

Overcoming Kernel Redundancy for Scaling Logic Gate Networks

Naive width scaling introduces redundant logic kernels that saturate performance, but dynamic kernel routing via gate-level Boolean operations restores scaling benefits and improves accuracy with better parameter efficiency.

Sejin Park, Hongjae Lee, Changwoo Han, Seung-Won Jung

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

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NeurIPS 2026U Toronto Layer6 AI Representation & contrastive learning

Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization

BloGDiT replaces joint Gaussian denoising with blocked Gaussian diffusion using iterative block resampling and annealed block sizes to enable targeted edits for constraint optimization, matching or outperforming prior methods on Sudoku, graph coloring, MIS, and MaxCut.

Yudong Will Xu, Wenhao Li, Xiaoyu Wang, Scott Sanner 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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