Claims Browser

Machine learning provides value as a profit-scored correction to standard advice rather than as a replacement for it

"Machine learning therefore pays as a profit scored correction to standard advice, not as its replacement."

Accurate prediction does not by itself make recommended nitrogen rates more profitable

"However, it is usually judged on prediction accuracy, and accurate prediction does not by itself make the recommended rate more profitable."

Foundation models that use a full year of Earth observations can have their embeddings recomputed over much shorter windows (down to a single day) while retaining substantial value for land-use/land-cover mapping

"Keeping the encoder frozen, we recompute its embeddings over varying observation windows, from a full year down to a single day."

For phenology-separated classes like crop types, Tessera embeddings achieve 46% better performance than the best from-scratch models

"Where classes are separated by phenology, as for the crop types of PASTIS-R, they reach a mean Intersection-over-Union of $58.3$, about $46\%$ above the best from-scratch model."

Tessera embeddings remain markedly more label-efficient than from-scratch models even when classes are temporally stable

"On both datasets, Tessera embeddings remain markedly more label-efficient."

Contracting the observation window from one year to one month costs 39% of segmentation accuracy on crop classification but only 5% on stable land cover classes

"Contracting the window from one year to one month costs $39\%$ of the segmentation accuracy on PASTIS-R but only $5\%$ on DynamicEarthNet."

Single-day embeddings from foundation models can still classify land cover at 3.4 times the chance level

"Single-day embeddings still classify land cover in LUCAS at $3.4$ times the chance level."

Temporal coverage in Earth observation foundation models is a tunable cost rather than a fixed prerequisite, enabling near-real-time mapping and faster refresh cycles

"Our study shows that temporal coverage is therefore a tunable cost rather than a fixed prerequisite, opening regimes such as near-real-time mapping and faster land-use/land-cover refresh cycles."

Adaptive domain augmentation for inverse problems can be given a principled Bayesian justification that ensures robust inference under incomplete prior knowledge

"This provides a principled, Bayesian justification for adaptive domain augmentation and ensures robust inference for inverse problems under incomplete prior knowledge."

Post-composing a fixed number of pairwise distinct nonconstant polynomials with a generic polynomial of sufficiently large degree yields linearly independent polynomials

"post-composing a fixed number of pairwise distinct nonconstant polynomials with a generic polynomial of sufficiently large degree yields linearly independent polynomials"

The conjecture on polynomial composition generalizes Newman-Slater's theorem on powers of polynomials

"This generalizes Newman--Slater's theorem on powers of polynomials"

The polynomial composition conjecture has been proven for two polynomials and for an arbitrary number of polynomials when their degrees are bounded

"We prove the result for two polynomials, and for an arbitrary number of polynomials when their degrees are bounded"

The polynomial composition conjecture implies a complete understanding of the identifiability of deep fully connected neural network architectures with generic polynomial activation functions

"the conjecture implies a complete understanding of the identifiability (i.e., parameter symmetries) of deep fully connected neural network architectures with generic polynomial activation functions"

For neural network architectures with layer-specific activations of increasing degree, the established versions of the conjecture fully characterize the set of parameters yielding the same end-to-end network function

"for network architectures with layer-specific activations of increasing degree, our established versions of the conjecture fully characterize the set of parameters yielding the same end-to-end network function"

The identifiability of shallow polynomial networks has been fully resolved

"we fully resolve the identifiability of shallow polynomial networks"

Directed graph learning with nonsymmetric propagation operators can be theoretically certified and controlled using a two-sided cone Rayleigh framework for generalized pencils

"Building on the two-sided cone Rayleigh framework for generalized pencils \[ B_θ-λG, \] we develop a learning-oriented methodology for spectral certification, sensitivity analysis, and control without requiring symmetry, nonnegativity, or cone preservation."

Computable cone bounds provide rigorous spectral enclosure with differentiable soft-min/max surrogates that maintain explicit approximation errors

"computable lower and upper cone bounds provide an a posteriori enclosure of a distinguished cone level, while smooth soft-min/max surrogates preserve rigorous one-sided bounds with explicit approximation errors and remain differentiable with respect to the trainable parameters."

Adaptive spectral control on the directed Cora citation network achieved approximately 21.5% reduction in spectral level under 0.5% edge-weight budget with no loss in test accuracy

"On the directed Cora citation network, adaptive recomputation of the right--left sensitivity reduces the distinguished spectral level by approximately $21.5\%$ under a cumulative edge-weight reduction budget of $0.5\%$, with no observed change in test accuracy for the trained model and data split considered."