2,458 research outputs found
Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements
In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin
Opportunities and risks of stochastic deep learning
This thesis studies opportunities and risks associated with stochasticity in deep learning that specifically manifest in the context of adversarial robustness and neural architecture search (NAS). On the one hand, opportunities arise because stochastic methods have a strong impact on robustness and generalisation, both from a theoretical and an empirical standpoint. In addition, they provide a framework for navigating non-differentiable search spaces, and for expressing data and model uncertainty. On the other hand, trade-offs (i.e., risks) that are coupled with these benefits need to be carefully considered. The three novel contributions that comprise the main body of this thesis are, by these standards, instances of opportunities and risks.
In the context of adversarial robustness, our first contribution proves that the impact of an adversarial input perturbation on the output of a stochastic neural network (SNN) is theoretically bounded. Specifically, we demonstrate that SNNs are maximally robust when they achieve weight-covariance alignment, i.e., when the vectors of their classifier layer are aligned with the eigenvectors of that layer's covariance matrix. Based on our theoretical insights, we develop a novel SNN architecture with excellent empirical adversarial robustness and show that our theoretical guarantees also hold experimentally.
Furthermore, we discover that SNNs partially owe their robustness to having a noisy loss landscape. Gradient-based adversaries find this landscape difficult to ascend during adversarial perturbation search, and therefore fail to create strong adversarial examples. We show that inducing a noisy loss landscape is not an effective defence mechanism, as it is easy to circumvent. To demonstrate that point, we develop a stochastic loss-smoothing extension to state-of-the-art gradient-based adversaries that allows them to attack successfully. Interestingly, our loss-smoothing extension can also (i) be successful against non-stochastic neural networks that defend by altering their loss landscape in different ways, and (ii) strengthen gradient-free adversaries.
Our third and final contribution lies in the field of few-shot learning, where we develop a stochastic NAS method for adapting pre-trained neural networks to previously unseen classes, by observing only a few training examples of each new class. We determine that the adaptation of a pre-trained backbone is not as simple as adapting all of its parameters. In fact, adapting or fine-tuning the entire architecture is sub-optimal, as a lot of layers already encode knowledge optimally. Our NAS algorithm searches for the optimal subset of pre-trained parameters to be adapted or fine-tuned, which yields a significant improvement over the existing paradigm for few-shot adaptation
Meta-learning algorithms and applications
Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples.
Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number.
Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation.
More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
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