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- W4281721388 abstract "The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same time, the development of equivariant methods, which preserve their output under the action of rotation or some other transformation, has proven to be important for geometry problems across these domains. In this work, we observe that Adam $-$ when treated as a function that maps initial conditions to optimized results $-$ is not rotation equivariant for vector-valued parameters due to per-coordinate moment updates. This leads to significant artifacts and biases in practice. We propose to resolve this deficiency with VectorAdam, a simple modification which makes Adam rotation-equivariant by accounting for the vector structure of optimization variables. We demonstrate this approach on problems in machine learning and traditional geometric optimization, showing that equivariant VectorAdam resolves the artifacts and biases of traditional Adam when applied to vector-valued data, with equivalent or even improved rates of convergence." @default.
- W4281721388 created "2022-06-13" @default.
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- W4281721388 date "2022-05-26" @default.
- W4281721388 modified "2023-09-29" @default.
- W4281721388 title "VectorAdam for Rotation Equivariant Geometry Optimization" @default.
- W4281721388 doi "https://doi.org/10.48550/arxiv.2205.13599" @default.
- W4281721388 hasPublicationYear "2022" @default.
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