Good Papers

Showing papers from University of Texas Health Science Center at Houston Show all papers

67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026StanfordU Minnesota - Twin CitiesU Wisconsin - MadisonJohns HopkinsTexas A&M University - Colle3D generation

AnaDiffusion: Anatomically Compositional Latent Diffusion for Controllable 3D Brain MRI Generation

Tracy Han, Lulin Liu, Bangya Liu, Yuanhao Cai and 7 more

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

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U Texas Health Science Center atU Texas Health Center at HousonGeorgia Institute of TechnologyU Houston - Clear LakeTongjiBrain imaging & connectomics

OpenBrain: An Auditable Generated-Label Release for Whole-Brain MRI Parcellation

Qizhen Lan, Yu-Chun Hsu, YUXIANG WEI, Lijing Zhu and 4 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U Texas Health Science Center atU Alabama at BirminghamU PittsburghThe University of Texas MD AnderU Dublin, Trinity CollegeBrain imaging & connectomics

BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning

Qizhen Lan, Mengchen Fan, Hang Zhang, Jingwei Duan and 4 more

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

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
89%Must read
?Must readVote to see the score
NeurIPS 2026Stevens Institute of TechnologyLambdaColumbiaU Texas Health Science Center atStevens Institute of Technology;LLM pretraining & scaling laws

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation

RISE sketches LLM output-layer influence hotspots into compressed dual-channel sketches, reducing storage up to 112x versus gradient methods while scaling to 32B parameters for attribution and data valuation.

yide ran, Jianwen Xie, Minghui Wang, W. Jim Zheng and 3 more

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

– ReadersNo votes yet
16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5