Good Papers

Showing papers from Mines ParisTech Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026CNRS LAGEPP Lyon1Mines Paris - PSLL2S, CNRSMines ParisTechNaver Labs EuropeTime series forecasting

Provable State Estimation with Recurrent Models

Vincent Andrieu, Pauline Bernard, Lucas Brivadis, Laurent Praly and 1 more

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

– ReadersNo votes yet
0/20 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: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Mines Paris & École Normale Mines ParisTechGANs & VAEs

A Critical $\beta$-Scale for Posterior Collapse in Dirichlet $\beta$-VAEs

Delphine Doutsas, Bruno Figliuzzi

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

– ReadersNo votes yet
0/20 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: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CeMM & Medical University ofMines ParisTechCeMM Research Center for MoleculStanfordGenomics & single-cell

Designing Cell-Type-Specific Regulatory DNA with Guided Discrete Diffusion

Animesh Awasthi, Martin Stoll, Raphael Bednarsky, Moritz Schaefer and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · 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
74%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Sorbonne Université - Faculté deMines ParisTechCNRSUniversité Claude BernardGenomics & single-cell

Likelihood-free inference of phylogenetic tree posterior distributions

A likelihood-free neural network estimates phylogenetic tree posteriors via sequence pair encodings and subtree merges, outperforming likelihood-based methods especially for intractable evolutionary models.

Luc Blassel, Noémie Sauvage, Pierre Barrat-Charlaix, Bastien Boussau and 2 more

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

– ReadersNo votes yet
9/20 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: 9 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 2/5
76%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026InriaMines ParisTechInria Grenoble - Rhône-AlpesBayesian & probabilistic methods

Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference

Annealed Langevin dynamics replaces biased reverse-SDE sampling for compositional SBI scores with controllable bridging densities, yielding explicit hyperparameter rules; Linhart et al.'s formulation allows larger steps and fewer iterations than Geffner et al.'s in Gaussian settings and generalizes

Camille Touron, Gabriel Cardoso, Julyan Arbel, Pedro Rodrigues

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

– ReadersNo votes yet
10/20 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: 10 of 20 reviewers recommend it
lenient 2/5
medium 7/10
strict 1/5