Biography
Sidath Handurukande is the team leader of the Cloud Core Network team at Huawei Ireland Research Center in Dublin. Sidath holds a PhD from the École Polytechnique Fédérale de Lausanne (Switzerland) and an MBA from Michael Smurfit Graduate Business School (Ireland). During his career Sidath has worked for several multinationals R&D centers. He has been committed to the design and development of data-driven AI systems for Service Quality Monitoring and Failure Analytics in telecom networks. Based on this work, he authored international patents and peer-reviewed conference/journal papers. Currently, at Huawei Ireland Research Centre, Sidath is playing an instrumental role in the design of Networks with Autonomous properties to enable the digital world of tomorrow.
https://scholar.google.com/citations?user=OX04-VUAAAAJ&hl=en&oi=ao
https://ie.linkedin.com/in/sidathhandurukande
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Topics
Artificial Intelligence; Machine Learning; Signal ProcessingBiography
Dr Varun Ojha is a researcher in artificial intelligence who primarily works on neural networks and data science. Dr Ojha is currently a lecturer in Computer Science at the University of Reading, UK. Previously as a Postdoctoral Fellow at the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, Dr Ojha led a team of researchers to investigate the human perception of dynamic urban environments using machine learning. Before this, Dr Ojha was a Marie-Curie Fellow at the Technical University of Ostrava, Czech Republic. Dr Ojha received a PhD degree in Computer Science from the Technical University of Ostrava, the Czech Republic. Earlier, Dr Ojha received a research fellowship position funded by the Govt of India’s Dept of Science and Technology at the Visvabharati University, India, to develop an intelligent mixed toxic gases pattern recognition system. Dr Ojha has 60+ research publications in international peer-reviewed journals and conferences.
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Simpler a model is better is its generalization. This research work presents a class of neural inspired algorithms that are highly sparse in their architectural construction but perform highly accurately. In addition, they make a simultaneous function approximation and feature selection when solving machine learning tasks: classification, regression, and pattern recognition. This class of algorithms are Neural Tee Algorithms: Heterogeneous Neural Tree, Multi-Output Neural Tree, and Backporgation Neural Tree. This research found that any such arbitrarily constructed neural tree, which is like an arbitrarily “thinned” neural network, has the potential to solve machine learning tasks with an equivalent or better degree of accuracy than a fully connected symmetric and systematic neural networks architecture. The algorithm takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. The algorithm produces an ad hoc neural tree which is trained using a stochastic gradient descent optimizer. The algorithms produce high-performing and parsimonious models balancing the complexity with descriptive ability on a wide variety of machine learning problems
Resources:
- Ojha, V., & Nicosia, G. (2022). Backpropagation neural tree. Neural Networks, 149, 66-83: https://arxiv.org/pdf/2202.02248.pdf
- Ojha, V., & Nicosia, G. (2020). Multiobjective optimization of multi-output neural trees. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE Press: https://arxiv.org/pdf/2010.04524.pdf
- Ojha, V. K., Abraham, A., & Snášel, V. (2017). Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming. Applied Soft Computing, 52, 909-924: https://arxiv.org/pdf/1705.05592.pdf
Sensitivity analysis offers the opportunity to explore the sensitivity (influence) of parameters on a model. This work applies global sensitivity analysis to deep learning and optimization algorithms for the analysis of the influence of their hyperparameters. For deep learning, we analyzed hyperparameters such as type of optimizers, learning rate, batch size, etc. We analyzed these hyperparameters for deep neural networks such as ResNet18, AlexNet, and GoogleNet. For the optimization algorithms, we analyzed hyperparameters of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. We investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tuneable hyperparameters, the framework reveals the behaviours of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.
Biography
Simone Scardapane is Assistant Professor at Sapienza University of Rome, working in several subfields of deep learning, including graph neural networks and continual learning. He has a strong interest in promoting machine learning in Italy, having contributed to several no-profit activities, including Meetups and podcasts. He is action editor of Neural Networks and Cognitive Computation, member of the IEEE CIS Social Media Sub-Committee, the IEEE Task Force on Reservoir Computing, the “Machine learning in geodesy” joint study group of the International Association of Geodesy, and chair of the Statistical Pattern Recognition Techniques TC of the International Association for Pattern Recognition.
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Automatic differentiation (autodiff) is at the hearth of the deep learning “magic”, and it is also powering advances in multiple fields ranging from visual rendering to quantum chemistry. In the first part of this practical tutorial, we show some fundamental ideas from the autodiff field, and how they are implemented in several common frameworks, including TensorFlow, PyTorch, and JAX. In the second part, we show instead how to implement a custom PyTorch-like autodiff library from scratch. We conclude with some trends and advanced tools from the autodiff world, e.g., stateless models in PyTorch.