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Posts
Reproducibility and Training State Management in PyTorch: Best Practices and Tips Permalink
Published:
In the ever-evolving field of machine learning, reproducibility, and efficient training state management are crucial for research and practical applications. Whether you’re developing new models, experimenting with hyperparameters, or deploying solutions to production, ensuring that your results are consistent and that you can seamlessly resume training after interruptions can save you valuable time and resources. Read more
Can Artificial Neural Networks Truly Map Our Natural Neural Networks (the human brain)? Permalink
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Artificial Neural Networks (ANNs) have gained significant attention in the field of Deep Learning, driven by the belief that they can replicate the functioning of our brains. Geoffrey Hinton, a prominent figure in the development of ANNs, argues that the brain’s ability to learn through connection strengths serves as the foundation for creating intelligent machines. In a 2019 interview with Nicholas Thompson, Hinton emphasized the importance of mimicking this process in ANNs. This article delves into the perspectives of Geoffrey Hinton and Yann LeCun on the potential of ANNs to map our natural neural networks and examines their predictions for the future of artificial intelligence. Read more
Efficient Feature Selection using Random Adaptive Sub-space Learning (RASSEL): Python implementation Permalink
Published:
An appropriate mix of good core learners leads to a reduction in prediction error and improves the precision of a regression or a classification task. This technique is known as ensemble learning (aggregation). Read more
publications
Approximation Algorithms for Fair Range Clustering
Published in ICML 2023, Hawaii, USA, 2023
Approximation Algorithms for Fair Range Clustering Read more
Recommended citation: Hotegni, S.S., Mahabadi, S. and Vakilian, A., 2023, July. Approximation Algorithms for Fair Range Clustering. In International Conference on Machine Learning (pp. 13270-13284). PMLR.
Multi-Objective Optimization for Sparse Deep Multi-Task Learning
Published in IJCNN 2024, Yokohama, Japan, 2024
Multi-Objective Optimization for Sparse Deep Multi-Task Learning Read more
Recommended citation: Hotegni, S.S., Berkemeier, M., & Peitz, S., (2024). Multi-Objective Optimization for Sparse Deep Multi-Task Learning.
talks
Multi-Objective Optimization for Deep Neural Network Training using Weighted Chebyshev Scalarization Permalink
Published:
6th European Conference on Computational Optimization (EUCCO): The EUCCO conference series aims to bring scientists from computational optimization, algorithms for large-scale optimization problems and related applications together. The 2023 edition especially emphasized optimization with partial differential equations, large-scale optimization, as well as numerical optimization algorithms and software. More information here Read more
Multi-Objective Optimization for Sparse Deep Multi-Task Learning Permalink
Published:
IEEE WCCI 2024 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The International Joint Conference on Neural Networks (IJCNN), the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and the IEEE Congress on Evolutionary Computation (IEEE CEC). More information here Read more