Null detection of 0.3-micron artificial grains in 1 m³ lunar regolith excludes Solar-type stars dispersing more than ~0.09 Earth masses of long-lived technomaterial over Galactic history.
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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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representative citing papers
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.
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.
Active Spatial Guidance replaces injected positional embeddings in ViTs with a training-only 2D coordinate regression loss on final-layer tokens, yielding better results than learned absolute or rotary embeddings on ImageNet-100, ADE20K, and Hypersim under matched training.
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.
Prologue adds a small set of learnable tokens trained exclusively with AR cross-entropy loss to decouple generation from reconstruction in autoregressive visual models, yielding lower gFID on ImageNet 256x256.
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.
citing papers explorer
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Disentanglement Beyond Generative Models with Riemannian ICA
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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STRABLE: Benchmarking Tabular Machine Learning with Strings
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.
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OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
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.
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Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
RAT reformulates regularized natural policy gradients as vanilla gradients with a transformed advantage, computed efficiently via randomized block Kaczmarz iterations on on-policy data.
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Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era
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.
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Navigating Potholes with Geometry-Aware Sharpness Minimization
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.
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Hyperbolic Concept Bottleneck Models
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.
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Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms
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.
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CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry
CascadeFormer tapers Transformer width with depth based on gradient fan-in asymmetry to match uniform baselines in perplexity while cutting latency 8.6%.
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What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Location embeddings from geographic INRs can be decomposed into sparse latent concepts, natural language concepts, and visual features while retaining high reconstruction capability.
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What changes after deployment? A survey on On-device Learning in TinyML
A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
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The Diffusion Encoder
A diffusion model serves as the encoder in an autoencoder when trained alternately with the decoder to resolve opposing update directions while retaining the standard diffusion training objective.
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MC$^2$: Monte Carlo Correction for Fast Elliptic PDE Solving
MC² corrects low-budget Monte Carlo solutions for elliptic PDEs with a single-pass neural network to match the accuracy of 1000× more Monte Carlo samples while outperforming classical and learned baselines.
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Towards interpretable AI with quantum annealing feature selection
Quantum annealing solves a combinatorial feature-map selection problem for CNNs, yielding improved class disentanglement over GradCAM and GradCAM++ in the reported evaluation.
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Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
SOLAR prevents latent rehearsal decay in online continual SSL by adaptively managing replay buffers with deviation proxies and an explicit overlap loss, delivering both fast convergence and state-of-the-art final accuracy on vision benchmarks.
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Drifting Fields are not Conservative
Drift fields are not conservative except for Gaussian kernels; sharp normalization makes them conservative for any radial kernel by equating them to score differences of kernel density estimates.
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Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
Shuffled DP-SGD requires σ ≥ 1/√(2 ln M) or κ ≥ (1/√8)(1 - 1/√(4π ln M)) to limit adversarial advantage, preventing strong privacy and high utility simultaneously.
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Conservation Laws for Modern Neural Architectures
Unified framework characterizes conservation laws for gradient flow in feedforward networks with GELU/SiLU/SwiGLU, multihead attention with positional encodings, and MoE models under various gating.
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Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
Marchenko-Pastur random-matrix pruning of DNNs yields theoretical certificates for accuracy preservation under small fine-tuning and empirical ImageNet results with 50-60% MAC reduction and sub-2pp accuracy drops on ViT and CNN models.
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Refresh-Scaling the Memory of Balanced Adam
Setting β in balanced Adam to achieve a refresh count R_β ≈1000 based on effective learning horizon T_ES improves validation robustness over fixed-β baselines across 11 vision and language experiments.
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MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
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Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
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Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
FedUCA formalizes the server as an optimizer that uses utility-constrained stochastic aggregation to maximize client retention and global performance in heterogeneous federated learning.
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SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions
NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.