Saved in:
| Main Author: | Theis, Lucas |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.04493 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Efficient Bayesian Inference from Noisy Pairwise Comparisons
by: Aczel, Till, et al.
Published: (2025)
by: Aczel, Till, et al.
Published: (2025)
What makes unlearning hard and what to do about it
by: Zhao, Kairan, et al.
Published: (2024)
by: Zhao, Kairan, et al.
Published: (2024)
Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment
by: Yu, Shaode, et al.
Published: (2024)
by: Yu, Shaode, et al.
Published: (2024)
annbatch unlocks terabyte-scale training of biological data in anndata
by: Gold, Ilan, et al.
Published: (2026)
by: Gold, Ilan, et al.
Published: (2026)
What makes an Ensemble (Un) Interpretable?
by: Bassan, Shahaf, et al.
Published: (2025)
by: Bassan, Shahaf, et al.
Published: (2025)
What makes Models Compositional? A Theoretical View: With Supplement
by: Ram, Parikshit, et al.
Published: (2024)
by: Ram, Parikshit, et al.
Published: (2024)
What makes math problems hard for reinforcement learning: a case study
by: Shehper, Ali, et al.
Published: (2024)
by: Shehper, Ali, et al.
Published: (2024)
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
by: Klein, Dominik, et al.
Published: (2023)
by: Klein, Dominik, et al.
Published: (2023)
Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows
by: Mao, Runze, et al.
Published: (2025)
by: Mao, Runze, et al.
Published: (2025)
MCCE: Monte Carlo sampling of realistic counterfactual explanations
by: Redelmeier, Annabelle, et al.
Published: (2021)
by: Redelmeier, Annabelle, et al.
Published: (2021)
Generating realistic patient data
by: Brandt, Tabea, et al.
Published: (2025)
by: Brandt, Tabea, et al.
Published: (2025)
Beyond Predictions in Neural ODEs: Identification and Interventions
by: Aliee, Hananeh, et al.
Published: (2021)
by: Aliee, Hananeh, et al.
Published: (2021)
What makes a good feedforward computational graph?
by: Vitvitskyi, Alex, et al.
Published: (2025)
by: Vitvitskyi, Alex, et al.
Published: (2025)
De novo molecular structure elucidation from mass spectra via flow matching
by: Mqawass, Ghaith, et al.
Published: (2026)
by: Mqawass, Ghaith, et al.
Published: (2026)
What makes a good BIM design: quantitative linking between design behavior and quality
by: Ni, Xiang-Rui, et al.
Published: (2024)
by: Ni, Xiang-Rui, et al.
Published: (2024)
Improving realistic semi-supervised learning with doubly robust estimation
by: Pham, Khiem, et al.
Published: (2025)
by: Pham, Khiem, et al.
Published: (2025)
Round and Round We Go! What makes Rotary Positional Encodings useful?
by: Barbero, Federico, et al.
Published: (2024)
by: Barbero, Federico, et al.
Published: (2024)
High-Fidelity Image Compression with Score-based Generative Models
by: Hoogeboom, Emiel, et al.
Published: (2023)
by: Hoogeboom, Emiel, et al.
Published: (2023)
3W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wells
by: Vargas, Ricardo Emanuel Vaz, et al.
Published: (2025)
by: Vargas, Ricardo Emanuel Vaz, et al.
Published: (2025)
Unified Guidance for Geometry-Conditioned Molecular Generation
by: Ayadi, Sirine, et al.
Published: (2025)
by: Ayadi, Sirine, et al.
Published: (2025)
What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions
by: Di Salvo, Francesco, et al.
Published: (2024)
by: Di Salvo, Francesco, et al.
Published: (2024)
AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement
by: Qiu, Jiangjie, et al.
Published: (2026)
by: Qiu, Jiangjie, et al.
Published: (2026)
What makes a word hard to learn? Modeling L1 influence on English vocabulary difficulty
by: Martins, Jonas Mayer, et al.
Published: (2026)
by: Martins, Jonas Mayer, et al.
Published: (2026)
What does making money have to do with crime?: A dive into the National Crime Victimization survey
by: Anuyah, Sydney
Published: (2025)
by: Anuyah, Sydney
Published: (2025)
Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
by: Antipov, Egor, et al.
Published: (2026)
by: Antipov, Egor, et al.
Published: (2026)
Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
by: Sakalyan, Kristiyan, et al.
Published: (2025)
by: Sakalyan, Kristiyan, et al.
Published: (2025)
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation
by: Palma, Alessandro, et al.
Published: (2025)
by: Palma, Alessandro, et al.
Published: (2025)
Generating realistic global precipitation fields from modelled atmospheric circulation
by: Aich, Michael, et al.
Published: (2025)
by: Aich, Michael, et al.
Published: (2025)
Memory makes computation universal, remember?
by: Garrison, Erik
Published: (2024)
by: Garrison, Erik
Published: (2024)
Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
by: Vieira, João Paulo, et al.
Published: (2025)
by: Vieira, João Paulo, et al.
Published: (2025)
What is a good matching of probability measures? A counterfactual lens on transport maps
by: De Lara, Lucas, et al.
Published: (2025)
by: De Lara, Lucas, et al.
Published: (2025)
Challenging the Human-in-the-loop in Algorithmic Decision-making
by: Tschiatschek, Sebastian, et al.
Published: (2024)
by: Tschiatschek, Sebastian, et al.
Published: (2024)
CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarios
by: Garg, Raghav, et al.
Published: (2025)
by: Garg, Raghav, et al.
Published: (2025)
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
by: Mill, Leonid, et al.
Published: (2022)
by: Mill, Leonid, et al.
Published: (2022)
Simulating realistic short tandem repeat capillary electrophoretic signal using a generative adversarial network
by: Taylor, Duncan, et al.
Published: (2024)
by: Taylor, Duncan, et al.
Published: (2024)
Generative models for decision-making under distributional shift
by: Cheng, Xiuyuan, et al.
Published: (2026)
by: Cheng, Xiuyuan, et al.
Published: (2026)
Disentangled Representation Learning with the Gromov-Monge Gap
by: Uscidda, Théo, et al.
Published: (2024)
by: Uscidda, Théo, et al.
Published: (2024)
Analogy making as amortised model construction
by: Nagy, David G., et al.
Published: (2025)
by: Nagy, David G., et al.
Published: (2025)
LLM-SAA: LLM-persona Generated Distributions for Decision-making
by: Baek, Jackie, et al.
Published: (2026)
by: Baek, Jackie, et al.
Published: (2026)
Similar Items
-
Efficient Bayesian Inference from Noisy Pairwise Comparisons
by: Aczel, Till, et al.
Published: (2025) -
What makes unlearning hard and what to do about it
by: Zhao, Kairan, et al.
Published: (2024) -
Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment
by: Yu, Shaode, et al.
Published: (2024) -
annbatch unlocks terabyte-scale training of biological data in anndata
by: Gold, Ilan, et al.
Published: (2026) -
What makes an Ensemble (Un) Interpretable?
by: Bassan, Shahaf, et al.
Published: (2025)