The Unreasonable Effectiveness of

By Leo P. Kadanoff. 2000

192 citations recorded by the source corpus.

Subfields: Law, Political Science and International Relations, Natural Language Processing, Philosophy

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Citations in the corpus

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022, background): “, 2017), speech recognition (Zhang et al., 2020; Gulati et al., 2020), conversational modeling (Adiwardana et al.”
GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians (2023, methodology): “Our method also stands out in self-reenactment, with significantly lower perceptual differences in terms of LPIPS [50].”
BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models (2022, methodology): “It is obvious that the proposed BBDM method achieves the best FID performance on all of the four tasks, and gains competitive LPIPS scores.”
SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering (2023, unknown): “We compute the standard metrics PSNR, SSIM and LPIPS [44] to evaluate the quality of SuGaR’s rendering using our extracted meshes and their bound surface Gaus-sians.”
pixy: Unbiased estimation of nucleotide diversity and divergence in the presence of missing data (2020, methodology): “The calculation of summary 27 statistics is thus often the first step in a population genetic analysis, be it an exploratory 28 study, a test of an evolutionary hypothesis, or the training of a machine-learning model 29 (Hartl et al. 1997; Flagel et al. 2019; Hahn 2019).”
What you Feel, Is what you like Influence of Message Appeals on Customer Engagement on Instagram (2020, methodology): “The availability of such a large dataset is a signal for the effectiveness of the algorithm (Halevy, Norvig, and Pereira 2009).”
Can Recurrent Neural Networks Learn Nested Recursion? (2018, background): “For example, Karpathy et al. (2015) and Karpathy (2016) show that RNNs can model Shakespeare plays, Wikipedia articles, algebraic geometry papers and modules of the Linux source code.”
Invisible Backdoor Attacks on Deep Neural Networks Via Steganography and Regularization (2019, methodology): “• We introduce two metrics; one is the Perceptual Ad-versarial Similarity Score (PASS) [1] and the other is Learned Perceptual Image Patch Similarity (LPIPS) [2] to deﬁne invisibility for human users.”