3 citations · 8 across the 10 of their papers we have counts for
4 papers · 2 filters
Efficient Search for Customized Activation Functions with Gradient Descent
Lukas Strack, Mahmoud Safari, Frank Hutter
Different activation functions work best for different deep learning models. To exploit this, we leverage recent advancements in gradient-based search techniques for neural archite…
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler +4
The increasing size of language models necessitates a thorough analysis across multiple dimensions to assess trade-offs among crucial hardware metrics such as latency, energy consu…
Surprisingly Strong Performance Prediction with Neural Graph Features
Gabriela Kadlecová, Jovita Lukasik, Martin Pilát +4
Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Alt…
Multi-objective Differentiable Neural Architecture Search
Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler +3
Pareto front profiling in multi-objective optimization (MOO), i.e., finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives that req…