AutoML 2023 Workshop Track Submissions

OpenML-CTR23 – A curated tabular regression benchmarking suite
Sebastian Felix Fischer, Matthias Feurer, Bernd Bischl
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Evaluating supernets for neural architecture search
Gabriel Meyer-Lee, Nick Cheney
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Continual learning under domain transfer with sparse synaptic bursting
Shawn Lawrence Beaulieu, Jeff Clune, Nick Cheney
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Neural Growth and Pruning in Dynamic Learning Environments
Kaitlin Maile, Luga Hervé, Dennis George Wilson
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Distilled Pruning: Using Synthetic Data to Win the Lottery
Luke McDermott, Daniel Cummings
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Generating Neural Network Architectures with Conditional Graph Normalizing Flows
Lichuan Xiang, Łukasz Dudziak, Abhinav Mehrotra, Mohamed S Abdelfattah, Nicholas Donald Lane, Hongkai Wen
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Structural Pruning of Large Language Models via Neural Architecture Search
Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone
Python Wrapper for Simulating Multi-Fidelity Optimization on HPO Benchmarks without Any Wait
Shuhei Watanabe
- Published: 06 Jul 2023, Last Modified: 15 Aug 2023
- AutoML 2023 (Workshop)
- Readers: Everyone