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<p>1st International Workshop on</p>
<h1 class="mb-4 pb-0"><span>M</span>achine <span>L</span>earning for <span>I</span>rregular <span>T</span>ime <span>S</span>eries (ML4ITS2021)</h1>

<p class="mb-4 pb-0">Co-located with Virtual <a href="https://2021.ecmlpkdd.org/">ECML PKDD 2021</a></p>
<p class="mb-4 pb-0">Submission <a href =" https://easychair.org/conferences/?conf=ml4its2021">HERE</a> </p>
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<h2>Theoretical Foundations of Deep Selective State-Space Models" -</h2>
<p>Antonio Orvieto</p>
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<p>Structured state-space models (SSMs) have emerged as powerful foundational architectures for sequential data, offering excellent performance across diverse domains and scaling efficiently to billions of parameters. Recent advancements demonstrate that incorporating selectivity mechanisms, such as multiplicative interactions between inputs and hidden states, significantly enhances the performance of SSMs, even surpassing attention-powered foundation models in text-based tasks. Antonio's work provides theoretical grounding for these selectivity mechanisms, using Rough Path Theory to fully characterize the expressive power of selective SSMs. His analysis identifies gating mechanisms as crucial to the architecture and offers a closed-form description of their superior performance over previous models like S4. This theoretical framework opens avenues for future improvements in SSM variants, particularly in the role of cross-channel interactions.</p>
<p>Antonio's primary expertise lies in optimization for deep learning. His work focuses on improving the efficiency of deep learning technologies through the development of new architectures and training techniques grounded in theoretical knowledge. His research spans a wide range of applications, including natural language processing, biology, neuroscience, and music generation. Notably, his LRU architecture serves as the foundation for several variants of Google's Gemma language model.</p>
<p>He has published in prestigious conferences such as NeurIPS, ICML, ICLR, AISTATS, and CVPR. Antonio has also organized sessions and workshops, including the "Optimization for Data Science and Machine Learning" session at the International Conference on Continuous Optimization (ICCOPT) 2022 and the ICML 2024 Workshop on Next Generation of Sequence Modeling Architectures.</p>

<h2>About</h2>
<p>Antonio Orvieto is an independent group leader at the Max Planck Institute for Intelligent Systems and a principal investigator at the ELLIS Institute Tübingen, Germany. He holds a Ph.D. from ETH Zürich and has gained valuable experience at leading institutions including Google DeepMind, Meta, MILA, INRIA Paris, and HILTI. </p>


<!--<p> Alippi is an IEEE Fellow, Member of the Administrative Committee of the IEEE Computational Intelligence Society, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past associate editor of the IEEE Transactions on Emerging topics in computational intelligence, the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, the IEEE-Transactions on Neural Networks.
In 2018 he received IEEE CIS Outstanding Computational Intelligence Magazine Award, the 2016 Gabor award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. </p>
<p>Current research activity addresses adaptation and learning in non-stationary environments, graph learning and Intelligence for embedded, IoT and cyber-physical systems.</p>
<p>He holds 8 patents, has published one monograph book, 7 edited books and about 200 papers in international journals and conference proceedings.</p>-->

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