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Fabian Paischer

7 accepted papers

2026

ANTiC: Adaptive Neural Temporal In Situ Compressor

ICML 2026poster

The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial differential equations (PDEs) have reached the petabyte-to-exabyte scale. Transient simulations modeling Navier-Stokes equations, magnetohydrodynamics, plasm…

Cited by 0SourceScholar
2025

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

NeurIPS 2025poster

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next generation reactor design. Plasma turbulence is governed…

Cited by 0SourceScholar
2025

Parameter Efficient Fine-tuning via Explained Variance Adaptation

NeurIPS 2025poster

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 weights via low-rank adaptation (LoRA). Existing initialization strategies for LoRA often rely on singular value decompositio…

Cited by 13SourceScholar
2023

Learning to Modulate pre-trained Models in RL

NeurIPS 2023poster

Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific tasks, they insufficiently adapt to new tasks. In supervised learning, this adaptation problem is addressed by large-sc…

2023

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

NeurIPS 2023poster

Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive success stories in mastering partially observable en…

2022

History Compression via Language Models in Reinforcement Learning

ICML 2022spotlight

In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency. To avoid tr…

2022

WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models

NAACL 2022long

Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other langua…