most citedEvolutionary Architecture Search through Grammar-Based Sequence Alignment

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.NE20251 cited

Evolutionary Architecture Search through Grammar-Based Sequence Alignment

Adri Gómez Martín, Felix Möller, Steven McDonagh +5

Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover completely novel and perfo…

cs.CL2025

Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation

Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik +5

Small Language models (SLMs) offer an efficient and accessible alternative to Large Language Models (LLMs), delivering strong performance while using far fewer resources. We introd…

cs.LG2024

Hyperband-based Bayesian Optimization for Black-box Prompt Selection

Lennart Schneider, Martin Wistuba, Aaron Klein +3

Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks, especially in black-box settings where models are only accessible via…

cs.LG2024

Warmstarting for Scaling Language Models

Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4

Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies l…

cs.CL2024

Compressing Large Language Models with Automated Sub-Network Search

Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter +1

Large Language Models (LLMs) demonstrate exceptional reasoning abilities, enabling strong generalization across diverse tasks such as commonsense reasoning and instruction followin…