148 citations · 316 across the 12 of their papers we have counts for
12 papers
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…
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…
Curvature in the Looking-Glass: Optimal Methods to Exploit Curvature of Expectation in the Loss Landscape
Jed A. Duersch, Tommie A. Catanach, Alexander Safonov +1
Harnessing the local topography of the loss landscape is a central challenge in advanced optimization tasks. By accounting for the effect of potential parameter changes, we can alt…
Projective Integral Updates for High-Dimensional Variational Inference
Jed A. Duersch
Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over…
Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions
Niladri Das, Jed A. Duersch, Thomas A. Catanach
In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based…
Adaptive n-ary Activation Functions for Probabilistic Boolean Logic
Jed A. Duersch, Thomas A. Catanach, Niladri Das
Balancing model complexity against the information contained in observed data is the central challenge to learning. In order for complexity-efficient models to exist and be discove…