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Uniform Ck Approximation of G-Invariant and Antisymmetric Functions, Embedding Dimensions, and Polynomial Representations

arXiv 2403.01339 ↗First posted 2 Mar 2024cs.LG math.RTcited by 0

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Abstract (arXiv)

For any subgroup G of the symmetric group Sn on n symbols, we present results for the uniform Ck approximation of G-invariant functions by G-invariant polynomials. For the case of totally symmetric functions (G = Sn), we show that this gives rise to the sum-decomposition Deep Sets ansatz of Zaheer et al. (2018), where both the inner and outer functions can be chosen to be smooth, and moreover, the inner function can be chosen to be independent of the target function being approximated. In particular, we show that the embedding dimension required is independent of the regularity of the target function, the accuracy of the desired approximation, as well as k. Next, we show that a similar procedure allows us to obtain a uniform Ck approximation of antisymmetric functions as a sum of K terms, where each term is a product of a smooth totally symmetric function and a smooth antisymmetric homogeneous polynomial of degree at most binomn2. We also provide upper and lower bounds on K and show that K is independent of the regularity of the target function, the desired approximation accuracy, and k.

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References · 19

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7 more
  1. 2020Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum statescited by 15
  2. 2019On Deep Set Learning and the Choice of Aggregationscited by 13
  3. 2021Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatilitycited by 10
  4. 2021Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistrycited by 5
  5. 2022Towards Antisymmetric Neural Ansatz Separationcited by 2
  6. 2023Anti-symmetric Barron functions and their approximation with sums of determinantscited by 2
  7. 2018Set Aggregation Network as a Trainable Pooling Layercited by 1

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