20 citations · 27 across the 8 of their papers we have counts for
5 papers · 1 filter
NeuralDEM -- Real-time Simulation of Industrial Particulate Flows
Benedikt Alkin, Tobias Kronlachner, Samuele Papa +3
Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial…
Parameter Efficient Fine-tuning via Explained Variance Adaptation
Fabian Paischer, Lukas Hauzenberger, Thomas Schmied +3
Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned for a specific downstream task. The most common fine-tuning method is to update pretrained weigh…
Vision-LSTM: xLSTM as Generic Vision Backbone
Benedikt Alkin, Maximilian Beck, Korbinian Pöppel +2
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has…
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
Benedikt Alkin, Andreas Fürst, Simon Schmid +3
Neural operators, serving as physics surrogate models, have recently gained increased interest. With ever increasing problem complexity, the natural question arises: what is an eff…
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter +1
We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations withi…