README.md

Sustainable Machine Learning Reading Group

Reading group on Sustainable Machine Learning and ICT. We discuss papers every other week, usually on Wednesdays at 3.30PM CET. If interested reach out to Dustin or Raghav.

Table of Contents

1. Upcoming Meetings
2. Past Meetings
3. About
4. Contact

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Upcoming Meetings

We are on a break due to the holiday season. Next meeting will be on 17/01/2024.

Past Meetings

Meeting 16: 06/12/2023

• Sebastian Hammer Eliassen presented BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs (Chen et al. 2023)
• Paper

Meeting-15: 22/11/2023

• Eya Ben Chaaben presented Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation. (Arendt et al. 2019)
• Paper

Meeting-14: 10/05/2023

• Daniel Geißler presented Read the Signs: Towards Invariance to Gradient Descent's Hyperparameter Initialization (Wadi et al. 2023)
• Paper

Meeting-13: 25/07/2023

• Pedram Bakhtiarifard presented Neural Architecture Search without Training (Mellor et al. 2020)
• Paper

Meeting 12: 11/10/2023

• Dustin Wright will be presented Language Modeling is Compression (Delétang et al. 2023).
• Paper

Meeting-11: 27/09/2023

• Raghavendra Selvan presented Pruning vs Quantization: Which is Better? (Kuzmin et al. 2023).
• Paper

Meeting-10: 21/06/2023

• Tong Chen presented Sparse Polynomial Optimization and Its Application to Deep Neural Network.
• Paper

Meeting-9: 07/06/2023

• Pedram Bakhtiarifard presented Once-for-All: Train One Network and Specialize it for Efficient Deployment (Cai et al. 2020)
• Paper

Meeting-8: 24/05/2023

• Eya Ben Chaaben presented EnergyVis: Interactively tracking and exploring energy consumption for ML models (Shaikh et al. 2021)
• Paper

Meeting-7: 10/05/2023

• Daniel Geißler presented Dodging the Sparse Double Descent (Quétu and Tartaglione 2023)
• Paper

Meeting-6: 26/04/2023

• Sebastian Hammer Eliassen will present EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression (Liu et al. 2022)
• Paper

Meeting-5: 12/04/2023

• Emil Jørgensen Njor presented Data Aware Neural Architecture Search (Njor et al. 2023)
• Paper

Meeting-4: 29/03/2023

• Julian Schön presented Learning from Randomly Initialized Neural Network Features (Amid et al. 2022)
• Paper

Meeting-3: 16/03/2023

• Bo Zhou presented Overcoming Oscillations in Quantization-Aware Training (Nagel et al. 2022)
• Paper

Meeting-2: 01/03/2023

• Raghavendra Selvan presented 8-bit Optimizers via Block-wise Quantization (Dettmers et al. 2022)
• Paper

Meeting-1: 15/02/2023

• Dustin Wright presented An Information-Theoretic Justification for Model Pruning (Isik et al, 2022)
• Paper

About

The reading group is part of the dissemination activities related to two EU projects Raghav is involved with. European Union’s Horizon Europe Research and Innovation programme under grant agreements No. 101070284 (EnrichMyData) and No. 101070408 (SustainML).

Contact

For more details about the project, reading group, or for opportunities reach out to Raghavendra Selvan: raghav@di.ku.dk.