1 citations · 2 across the 21 of their papers we have counts for
5 papers · 1 filter
Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
Jed A. Duersch, Naïm Es-Sebbani, Nathanaël Haas +1
Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused multiply-accumulate (FMA) operat…
Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
Nathanaël Haas, François Gatine, Augustin M Cosse +1
Understanding why gradient-based training in deep networks exhibits strong implicit bias remains challenging, in part because tractable singular-value dynamics are typically availa…
Fourier Transformers for Latent Crystallographic Diffusion and Generative Modeling
Jed A. Duersch, Elohan Veillon, Astrid Klipfel +2
The discovery of new crystalline materials calls for generative models that handle periodic boundary conditions, crystallographic symmetries, and physical constraints, while scalin…
Structurally Separated Uncertainty in Supervised Latent Variable Models
Tanmoy Mukherjee, Marius Kloft, Pierre Marquis +1
Predictive uncertainty is commonly decomposed into epistemic and aleatoric components, but standard decompositions often produce strongly correlated estimates because both quantiti…
MODE: Multi-Objective Adaptive Coreset Selection
Tanmoy Mukherjee, Pierre Marquis, Zied Bouraoui
We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performa…