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A curated guide to studying deep learning with PyTorch via a full YouTube live course series, covering topics from tensors to GANs, organized into six parts.
This paper develops a sharp pseudospectral theory for block-triangular Jacobians in coupled gradient descent, proving Kreiss-constant bounds and establishing iteration complexity results. The work exposes non-asymptotic, instance-dependent transient amplification phenomena relevant to bilevel optimization, two-time-scale stochastic approximation, and GAN training.
This paper proposes CAT, a cross-scale aligned transformer that enforces consistency between intermediate and final GAN outputs to resolve trajectory misalignment, achieving state-of-the-art FID of 1.56 on ImageNet-256.
This paper systematically investigates privacy risks in generative models for trajectory data, identifying a gap in empirical privacy evaluation and demonstrating Membership Inference Attacks against representative models.
OT-GAN introduces a novel GAN variant using optimal transport combined with energy distance in an adversarially learned feature space to improve training stability and image generation quality. The method demonstrates state-of-the-art results on benchmark problems with stable training using large mini-batches.