RICA replaces ICA's global generative model with local Riemannian geometry, introducing a disentanglement tensor based on the Hessian of the log-likelihood and Ricci curvature to measure pointwise disentanglement, which recovers sources across manifolds in controlled tests.
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- dataset ratio as baseline first@τdivided by ours first@τ. A speedup ratio greater than 1.0×means ours reaches the same target earlier with fewer epochs or steps. For higher-is-better metrics (Top-1, AP50), first@τis the first epoch with metric at or aboveτ. For lower-is-better metrics (FID), first@τis the first step at or belowτ. Gate and Hyperparameter Selection.For ImageNet classification [7], we useτ= 65for ResNet-50 [14] andτ= 50for ViT-S/16 [8]. For CIFAR early-stage classification [26], we use fix
- dataset ϕ(cchild,c parent)< η text(∥˜ cparent∥)·ω(˜ cparent).(8) This allows users to prune entire branches of spurious concepts with a single interaction, substantially reducing the number of interventions required to correct a prediction. 4 Experiments We evaluate HypCBM across three domains:CIFAR-100[ 20] for general object classification, SUN397[ 51] for (hierarchical) scene understanding, andImageNet[ 6] to assess scalability to real- world complexity. Additional results onCUB-200[ 50] are provided
- background URL https://www.datanami.com/2020/07/06/ data-prep-still-dominates-data-scientists-time-survey-finds/. [8] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large- scale hierarchical image database. In2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, 2009. doi: 10.1109/CVPR.2009.5206848. [9] Robert Dorfman. A formula for the gini coefficient.The Review of Economics and Statistics, 61 (1):146-49, 1979. URL https://EconPapers.repec.org
- dataset The inverse-rendering model, invRend-BFM, was trained to infer the BFM generative parameters of a 2D face image, including identity-related shape and texture latents as well as expres- sion, pose, light direction, and light intensity. The object-categorization model, objCat-ImageNet, was trained to classify natural images into ImageNet object categories [46]. Details of the training objective, architectural modi- fications, and training dataset for each model are provided in Methods 4.1. For cop
- background by shared tasks, common data, and open leaderboards, was the engine behind transformative progress 2 Figure 1:MC 2 pipeline.A low-budget Monte Carlo WoS estimate is corrected in a single forward pass by a learned operator, yielding an improved solution for the PDE. in NLP and computer vision, where benchmarks like GLUE [35], SuperGLUE [36], and ImageNet [8] created a culture of head-to-head comparison on identical inputs. PDE solving has no analog. Existing benchmarks each occupy narrow regimes:
- background Instance Segmentation.Cityscapes [ 6], ADE20K [42], LVIS [12], and Mapillary Vistas [28] cover outdoor driving and general scenes but apply no domain-specific vocabulary tailored to commercial spaces-the escalators, retail shelves, display cases, hotel beds, and food presentations that define the majority of Urban-ImageNet's images. Scaling Behaviour.ImageNet [ 7] established scale as a performance driver; GPT-3 [4] and scaling laws [18] showed predictable growth; LAION-5B [35] demonstrated bill
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