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Träfflista för sökning "WFRF:(Johansson Simon 1994) srt2:(2023)"

Sökning: WFRF:(Johansson Simon 1994) > (2023)

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1.
  • Johansson, Simon, 1994, et al. (författare)
  • Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes
  • 2023
  • Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ; , s. 5260-5265
  • Konferensbidrag (refereegranskat)abstract
    • Determinantal point processes (DPPs) have become prominent in data summarization and recommender system tasks for their ability to simultaneously model diversity as well as relevance. In practical applications, k-Determinantal point processes (k-DPPs) are used to yield a selection of k items from a set of size N that are the most representative of the set. In this paper, we study a special case of the diverse subset selection problem where a fixed set GO is already given as a forced recommendation and the task is to determine the remainder of the recommendation G1. The standard k-DPP optimization objectives here can suggest items that are close to optimal when considering only items in G1, but are arbitrarily close to items in G0, i.e., they might not be sufficiently diverse w.r.t. G0. We explore a semi-supervised k-DPP objective that simultaneously considers G0 and G1 and compares the difference between the two recommendations. We demonstrate our findings using multiple examples where the diverse subset selection problem with forced recommendation is important in practice.
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2.
  • Johansson, Simon, 1994 (författare)
  • Intelligent data acquisition for drug design through combinatorial library design
  • 2023
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • A problem that occurs in machine learning methods for drug discovery is a need for standardized data. Methods and interest exist for producing new data but due to material and budget constraints it is desirable that each iteration of producing data is as efficient as possible. In this thesis, we present two papers methods detailing different problems for selecting data to produce. We invest- igate Active Learning for models that use the margin in model decisiveness to measure the model uncertainty to guide data acquisition. We demonstrate that the models perform better with Active Learning than with random acquisition of data independent of machine learning model and starting knowledge. We also study the multi-objective optimization problem of combinatorial library design. Here we present a framework that could process the output of gener- ative models for molecular design and give an optimized library design. The results show that the framework successfully optimizes a library based on molecule availability, for which the framework also attempts to identify using retrosynthesis prediction. We conclude that the next step in intelligent data acquisition is to combine the two methods and create a library design model that use the information of previous libraries to guide subsequent designs.
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Johansson, Simon, 19 ... (2)
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