『targz』のカバーアート

targz

targz

著者： Luca Florio
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In targz we brigde the gap between industry and academia in the IT world. In every episode I will interview a researcher that will explain a paper that is representative of their work. We will try to keep it short an simple, so that anyone working in IT can enjoy and understand the paper, no Ph.D. required. Fasten your seat belt, open your mind, and get ready to unpack a tarball of compressed Computer Science knowledge!Luca Florio
エピソード
  • EP10 - Quantum Computing and Memory. With Dr. Alessandro Berti

    EP10 - Quantum Computing and Memory. With Dr. Alessandro Berti
    2026/05/25

    Talking about Quantum Computing always makes me feel like I am wearing a pair of glasses that let me see straight into the future. Join me in this episode of targz with Alessandro Berti, Postdoc at University of Pisa, where we talk about Quantum Algorithms and specifically the importance of (quantum) memory!

    If you want to dig further in the topic, you can read more in the paper "Efficient Quantum State Preparation with Bucket Brigade QRAM".
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    24 分
  • EP09 - The Thinking Process of LLMs. With Sara Marjanovic

    EP09 - The Thinking Process of LLMs. With Sara Marjanovic
    2026/05/11

    Have you ever thought about the thinking process of LLMs? Well, Sara Marjanovic, PhD student at University of Copenhagen, did it and she shares her research with us in this episode of targz!

    If you want to dig further in the topic, you can read more in the paper "DeepSeek-R1 Thoughtology: Let’s think about LLM reasoning".
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    15 分
  • EP08 - Fairness and Relevance in Recommender Systems. With Dr. Theresia Veronika Rampisela

    EP08 - Fairness and Relevance in Recommender Systems. With Dr. Theresia Veronika Rampisela
    2026/04/27

    Fairness and relevance may be diverging goals in recommender systems. Can we find a way to achieve both? We talk about it with Dr. Theresia Veronika Rampisela in this episode of targz!

    Do you want to dig more? Checkout the paper: "Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier"
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    14 分
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