Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Structural interpretability in SVMs using truncated orthogonal polynomial kernels reveals model complexity.
Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas
Structural interpretability in SVMs using truncated orthogonal polynomial kernels reveals model complexity.
Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas
Amortized Optimal Transport using sliced potentials enhances OT plan prediction efficiency across multiple measure pairs.
Minh-Phuc Truong, Khai Nguyen
Fast interpretable autoregressive estimation using neural network backpropagation, achieving 12.6x speedup.
Anaísa Lucena, Ana Martins, Armando J. Pinho et al.
OmniAnomaly and PCA perform comparably on the SMD dataset, especially without point adjustment.
Bruna Alves, Ana Martins, Armando J. Pinho et al.
Study the mechanism of diffusion models learning data statistics from simple to complex using the mixed cumulant model.
Lorenzo Bardone, Claudia Merger, Sebastian Goldt
VecMol generates 3D molecules using vector-field representations, avoiding explicit graph generation and enhancing geometry-chemistry coherence.
Yuchen Hua, Xingang Peng, Jianzhu Ma et al.
The paper refines and extends the batched kernelized bandits problem, optimizing batch numbers and regret bounds.
Chenkai Ma, Keqin Chen, Jonathan Scarlett
Efficient approximation of analytic and L^p functions using height-augmented ReLU networks, significantly improving approximation rates.
ZeYu Li, FengLei Fan, TieYong Zeng