Researchers at Lawrence Livermore National Laboratory (LLNL) Developed a Novel Deep Learning Framework for Symbolic Regression | MarkTechPost


At the Lawrence Livermore National Laboratory (LLNL), scientists have developed a novel framework and an accompanying visualization device that makes use of deep reinforcement studying for symbolic regression issues, outperforming baseline strategies on benchmark issues.

Their paper was just lately accepted as an oral presentation on the International Conference on Learning Representations (ICLR 2021). In their paper, the researchers describe making use of deep reinforcement studying to discrete optimization. Discrete optimization focuses on issues that take care of discrete “building blocks” that should be mixed in a selected order or configuration to optimize the specified property. They targeted on a kind of discrete optimization referred to as symbolic regression. Symbolic regression finds brief mathematical expressions that match knowledge gathered from an experiment. It goals to find the underlying equations or dynamics of a bodily course of.

According to the lead writer of the paper, Brenden Petersen, discrete optimization is difficult as a result of it doesn’t have gradients. He additional explains that they’ve proven that deep reinforcement studying is a robust technique to go looking the area of discrete objects effectively. The group has proposed to make use of giant fashions to discover the area of small fashions.

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Symbolic regression is often approached in machine studying and synthetic intelligence with evolutionary algorithms. The main subject with evolutionary approaches is that the algorithms aren’t principled and don’t scale very properly. LLNL’s deep studying strategy is exclusive as a result of it’s theory-backed and based mostly on gradient info, making it far more comprehensible and helpful for scientists.


“At the core of their strategy is a neural community that learns the panorama of discrete objects. It does so by holding a reminiscence of the method and understanding how these objects are distributed on this large area to resolve a wonderful path to comply with. This mixture of reminiscence and path is often lacking from typical strategies.

According to Santiago, a co-author, the DSR framework permits a variety of constraints to be thought-about, thereby significantly lowering the search area’s dimension. The group examined this algorithm on a set of symbolic regression issues. The outcomes confirmed that it outperformed a number of frequent benchmarks, together with industrial software program gold requirements.

The group has examined their framework on real-world physics issues equivalent to thin-film compression, the place it exhibits promising outcomes. According to researchers, the algorithm is extensively relevant and is not only restricted to symbolic regression. They have even began to use it to the creation of distinctive amino acid sequences for improved binding to pathogens for vaccine design. The group has additionally created an interactive visualization app for the algorithm that physicists can use to resolve real-world issues.



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