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Showing papers from Aalborg University Show all papers

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NeurIPS 2026SpotlightEast China NormalXian PolytechnicAalborgTime series forecasting

Rubato: Signature Attention for Irregular Multivariate Time Series Forecasting

Jintao Yang, Meng Wang, Chenjuan Guo, Bin Yang

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

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57%Worth a look
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NeurIPS 2026U Applied Sciences Upper AustriaAalborgTU Wien, USTPHFG KarlsruheBIOLOGIKDatasets & benchmarks

The BAMBI Dataset: Multimodal Nadir UAV-Recordings of Forest Wildlife

Christoph Praschl, Hugo Markoff, Anna Maschek, Wolfram Jantsch and 7 more

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

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lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026East China NormalCFETSCFETSITCfetsitAalborgTime series forecasting

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting

Xiangfei Qiu, Liu Yang, Xiangyu Xu, Hanyin Cheng and 8 more

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

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lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026U OsloAalborgHong Kong PolytechnicShandongU Oslo, Simula Research LaboratoEmbodied agents

REINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning

Zhihong Cui, Hengyu Liu, Haoran Tang, shijun liu 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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medium 0/10
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57%Worth a look
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NeurIPS 2026U OsloAalborgSimulaMetThe Education University of HongU Oslo, Simula Research LaboratoVision-language-action models

Locating and Repairing Domain Shift in VLM Trajectory Planning

Zhihong Cui, Hengyu Liu, Michael A. Riegler, Guandong Xu 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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lenient 1/5
medium 0/10
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45%Niche pick
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NeurIPS 2026AalborgPingCAP IncU OsloAnt GroupNortheasternHuman-AI interaction

Human–AI Collaboration Requires a High-Order Dynamic Abstraction Substrate

Hengyu Liu, Dongxu Huang, Zhihong Cui, Lun Du and 4 more

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

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57%Worth a look
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NeurIPS 2026AalborgU Roma "La Sapienza"Deep RL

RL-Guided Contraction of Symbolic Tensor Networks for Quantum Circuit Equivalence

Suhaib Al-Rousan, Christian Schilling, Max Tschaikowski, Kim Larsen

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

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lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026AalborgGenomics & single-cell

UncertainGen: Scalable Uncertainty-Aware Representation Learning of DNA Sequences

Abdulkadir Celikkanat, Andres Masegosa, Mads Albertsen, Thomas Nielsen

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

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lenient 1/5
medium 0/10
strict 0/5
72%Highly rated
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NeurIPS 2026East China NormalAalborgInception Institute of ArtificiaTime series classification & imputation

HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation

HyFAD couples time- and frequency-domain diffusion for time series imputation, using frequency-aware step embeddings to improve high-frequency reconstruction and achieve state-of-the-art results.

Hongfan Gao, Wangmeng Shen, Bin Yang, Jilin Hu

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

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AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 0/5
72%Highly rated
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NeurIPS 2026Harbin Institute of Technology ,Harbin Institute of TechnologyHarbin Institute of TechnologyHITThe Chinese University of Hong KVision-language-action models

ATI-VLA: Action-Centric Predictive Vision–Language–Action Models via Actionable Alignment Then Adaptive Injection

ATI-VLA aligns predictive observations and actions in a shared discrete codebook, then adaptively injects predictive latents into action decoding, achieving state-of-the-art robotic manipulation with faster convergence.

Yijie Zhu, Rui Shao, Jie He, Wei Li and 7 more

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

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lenient 5/5
medium 3/10
strict 0/5
70%Highly rated
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NeurIPS 2026U AlmeríaAalborgBayesian & probabilistic methods

Flow-Transformed Implicit Processes for Function-Space Variational Inference

FTIP replaces Gaussian variational weights with normalizing flows for implicit process priors, capturing asymmetric and multimodal function-space posteriors.

Luis Antonio Ortega Andrés, Andres Masegosa, Thomas Nielsen

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

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5/20 AI panelreviewers recommend it

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AI panel: 5 of 20 reviewers recommend it
lenient 3/5
medium 1/10
strict 1/5
71%Highly rated
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NeurIPS 2026East China NormalInception Institute of ArtificiaAalborgTime series foundation models

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

FLAME is a lightweight time series foundation model using Legendre Memory and normalizing flows for efficient probabilistic forecasting with strong benchmark performance.

Xingjian Wu, Hanyin Cheng, Xiangfei Qiu, Zhengyu Li and 3 more

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

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AI panel: 6 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 0/5
88%Must read
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NeurIPS 2026Aalborg University Copenhagen SeAalborgLLM evaluation & benchmarks

Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects

A generalized extraction framework proves diffusion language model memorization rises with sampling resolution, and they leak less personally identifiable information than autoregressive models.

Xiaoyu Luo, Wenrui Yu, Qiongxiu Li, Johannes Bjerva

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 3/5
70%Highly rated
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NeurIPS 2026CNRSAalborgAutomation & Control, DepartLearning theory

PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise

PAC-Bayesian bounds relate expected and empirical prediction errors for partially observed LTI state-space systems with sub-Gaussian noise, yielding finite-sample guarantees for system identification and parameter estimation.

Mihaly Petreczky, Mohamad Al Ahdab, John Leth

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

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4/20 AI panelreviewers recommend it

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AI panel: 4 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 1/5