most citedEstimating Canopy Height at Scale

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cs.LG2026

Neural Field Tokenizations with Hierarchy and Spatial Locality Priors

Alonso Urbano, David W. Romero, Max Zimmer +1

Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities. Existing approaches are domina…

cs.LG2026

A Free Lunch in LLM Compression: Revisiting Retraining after Pruning

Moritz Wagner, Christophe Roux, Max Zimmer +1

Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since global retraining is expensive at…

cs.LG2026

RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization

Alonso Urbano, David W. Romero, Max Zimmer +1

Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group fixed a priori. Class-pose decompositions aim to create di…

cs.LG2026

The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning

Max Zimmer, Nico Pelleriti, Christophe Roux +1

AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday resear…

cs.LG2026

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

Jan Pauls, Max Zimmer, Berkant Turan +4

With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, an…

cs.LG2026

From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs

Louis Schiekiera, Max Zimmer, Christophe Roux +2

We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer mod…