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20212026
most citedLossy Neural Compression for Geospatial Analytics: A Review

5 citations · 11 across the 20 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models

Rania Briq, Ohad Fried, Michael Kamp +1

Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples influence the generated image th…

cs.LG2026

Fourier Neural Operators for Rayleigh-Bénard Convection

Chelsea Maria John, Thibaut Lunet, Sebastian Götschel +3

We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving high…

cs.LG2026

Exploring and Exploiting Stability in Latent Flow Matching

Rania Briq, Michael Kamp, Ohad Fried +2

In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage. We characterize…

cs.LG2025

Optimal Scaling Needs Optimal Norm

Oleg Filatov, Jiangtao Wang, Jan Ebert +1

Despite recent progress in optimal hyperparameter transfer under model and dataset scaling, no unifying explanatory principle has been established. For Adam and Scion optimizers, w…

cs.LG2025

Polynomial, trigonometric, and tropical activations

Ismail Khalfaoui-Hassani, Stefan Kesselheim

Which functions can be used as activations in deep neural networks? This article explores families of functions based on orthonormal bases, including the Hermite polynomial basis a…

cs.LG2024

Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit

Oleg Filatov, Jan Ebert, Jiangtao Wang +1

One of the main challenges in optimal scaling of large language models (LLMs) is the prohibitive cost of hyperparameter tuning, particularly learning rate and batch size . W…

cs.LG2023★ 4 cited

Tokenizer Choice For LLM Training: Negligible or Crucial?

Mehdi Ali, Michael Fromm, Klaudia Thellmann +18

The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and…