Good Papers

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57%Worth a look
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NeurIPS 2026Massachusetts Institute of TechnMITHarvardNew YorkInstituto de Astrofisica De CanaPhysics-informed ML & PDEs

Learning What's Real: Disentangling Signals and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

Pablo Mercader-Perez, Carolina Cuesta Lazaro, Daniel Muthukrishna, Jeroen Audenaert 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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lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026StanfordFremont Unified School DistrictStanfordMIT/GoogleText-to-image

Characterizing the Aesthetic Defaults of Generative Image Models

Maty Bohacek, Raina Panda, Daniel Fein, Arpita Singhal and 3 more

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

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86%Must read
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NeurIPS 2026Massachusetts Institute of TechnAdobe ResearchAdobeAdobe Research, USMITDiffusion models

End-to-End Training for Unified Tokenization and Latent Denoising

UNITE unifies tokenization and latent diffusion via a shared generative encoder, enabling single-stage joint training from scratch without adversarial losses or pretrained encoders to reach near state-of-the-art FID scores.

Shivam Duggal, Xingjian Bai, Zongze Wu, Richard Zhang and 4 more

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

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 2/5