Links
Efficient and Sustainable Machine Learning:
- Google Research, 2022 & beyond: Algorithms for efficient deep learning.
- MIT HAN Lab: Efficient AI computing lab at MIT.
- DC-Bench: A standardized benchmark for dataset condensation.
- AutoML: Automation of machine learning workshop.
- Numenta: A young company about neuroscience and AI.
- Nature: In AI, is bigger always better?.
Seminars on Optimization:
- POEMA: Polynomial Optimization, Efficiency through Moments and Algebra.
- OWOS: One World Optimization Seminar.
- SPOT: Séminaire Pluridisciplinaire d’Optimisation de Toulouse.
- BrainPOP: brainstorming days on measure and polynomial optimization.
Polynomial Optimization Software:
- Gurobi: mathematical programming solver.
- Mosek: large scale optimization software.
- Yalmip: a toolbox for modeling and optimization in MATLAB.
- Gloptipoly: generalized problem of moments (GPM) solver.
- TSSOS: polynomial optimization tool based on moment-SOS hierarchy.
- STRIDE: SpecTrahedRal Inexact projected gradient Descent along vErtices.
Robustness Verification Software:
- AutoAttack: State-of-the-art automated adversarial attacks.
- Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models by PyTorch, TensorFlow, and JAX.
- jax_verify: Neural network verification in JAX.
- ERAN: ETH Robustness Analyzer for Neural networks.
- alpha, beta-CROWN: A fast and scalable neural network verifier with efficient bound propagation.
- SoK: Certified robustness for deep neural networks.
- RobustBench: A standardized benchmark for adversarial robustness using AutoAttack (which is the starting point of RobustBench and is no longer maintained.
- RobustML: A community-run hub for learning about robust machine learning.
- convex_adversarial: Training provably robust neural networks by optimizing convex outer bounds on the adversarial polytope.
- RobustNeuralNetworks.jl: A Julia package for robust neural networks built from the Recurrent Equilibrium Network (REN) and Lipschitz-Bounded Deep Network (LBDN) model classes.
Workshops and Tutorials on Verification:
- WFVML: Workshop on Formal Verification of Machine Learning.
- VNN-COMP: International Verification of Neural Networks COMPetition.
- Adversarial Robustness - Theory and Practice by Zico Kolter and Aleksander Madry.
- Reliable and Interpretable Artificial Intelligence by Martin Vechev.
- Trustworthy Machine Learning by Reza Shokri and Nicolas Papernot.