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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A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
OncoTraj releases a harmonized 813-patient dataset with audited splits for three tasks on osimertinib resistance, showing single-timepoint NGS features yield no model above chance while recovering a TP53 association.
iSAGE achieves near-dense mIoU performance in remote sensing semantic segmentation using iterative expert clicks on confident model errors with an error-weighted loss, using only 0.011-0.04% of pixels.
Smaller self-supervised ViTs localize objects better via attention than larger ViTs, enabling A² to decouple localization from feature extraction for competitive performance on distribution-shifted benchmarks.
STROP learns variable-length discrete visual programs for images by training a length head against frozen DINOv3 features in a four-phase curriculum while bypassing pixel reconstruction.
HyperDn is a configuration-conditioned predictor that transfers oracle supervision across denoising paradigms to achieve near-oracle hyperparameter prediction with few or zero target labels.
SDM is a new staged gradient attack that reconstructs the adversarial objective around probability differences and reports stronger performance than prior methods like APGD.
RAT reformulates regularized natural policy gradients as vanilla gradients with a transformed advantage, computed efficiently via randomized block Kaczmarz iterations on on-policy data.
Formalizes Reasoning Portability (RP) and proposes RDB-CL to modulate per-sample KL regularization in RLVR for MLLM continual learning, achieving +12.0% Last accuracy over vanilla RLVR baseline by preserving reusable reasoning on high-RP samples.
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
LLQR+SAM pairs a slow learned geometry preconditioner with fast SAM perturbations to amplify escape from locally sharp 'potholes' while stabilizing flat basins, producing consistent gains over SAM and LLQR alone.
Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
MMM-Bench supplies 5,990 multi-modal documents from 12 commercial domains annotated along a 5-level taxonomy to test document classification under realistic business conditions.
Urban-ImageNet is a 2-million-image multi-modal dataset with HUSIC 10-class taxonomy enabling benchmarks for urban scene classification, cross-modal retrieval, and instance segmentation.
HypCBM reformulates concept activations as geometric containment in hyperbolic space to produce sparse, hierarchy-aware signals that match Euclidean models trained on 20 times more data.
A modified DCGAN with an auxiliary discriminator using the membrane factor generates stable, previously unseen funicular shells optimized for pure compression in three dimensions.
A diffusion-based pipeline creates a 27M-annotation dataset of object placements that outperforms human annotations and baselines on image editing tasks, then distills it into a fast model.
LOGGIA is a delay-aware graph neural routing algorithm using pre-training and RL that outperforms shortest-path and other neural methods in realistic network simulations.
XR Blocks supplies an LLM-optimized Reality Model and Vibe Coding XR workflow that converts high-level prompts into working physics-aware XR applications with high one-shot success.
Matched benchmarking reveals FID misleads in few-step regimes under CFG, prompting CLIP-scaled and PickScore-scaled FID and IS variants for better semantic evaluation of one-step image generators.
DREAM introduces Masking Warmup and Semantically Aligned Decoding to let a single encoder handle both contrastive alignment and masked generation, yielding gains over CLIP and FLUID on understanding and generation benchmarks.
MobileMold provides 4941 smartphone microscopy images and shows deep learning models reach 99.5% accuracy on mold detection and food classification tasks.
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Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
Unified World Models couple video and action diffusion inside one transformer with independent timesteps, enabling pretraining on heterogeneous robot datasets that include action-free video and producing more generalizable policies than imitation learning alone.