Search Results for author:
Found 4 papers, 3 papers with code
Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions.
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Here, we focus on the latter, the susceptibility of federated learning to various data corruption attacks.
Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation
We focus on the question of how the emulator learns the rules of quantum dynamics from the curriculum of simple training examples and to which extent it can generalize the acquired knowledge to solve more challenging cases.
Inferring topological transitions in pattern-forming processes with self-supervised learning
The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains.
