arXiv AI
Jul 21
DepthART: Scaling Foundation Monocular Depth to Tiny Models
arXiv:2607. 17099v1 Announce Type: cross Abstract: Recent geometric foundation models (e.
When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark
arXiv:2606. 09705v1 Announce Type: new Abstract: Scientific generative modeling often requires size transfer, where models trained on small systems are evaluated on larger ones.
Related stories
arXiv Machine Learning
6d ago
Small-Scale Experiments: Are We There Yet?
arXiv:2608. 11859v1 Announce Type: new Abstract: Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver.
arXiv Machine Learning
Aug 3
Who Wins Where? Conformal Model Comparison for Local Superiority
arXiv:2607. 29053v1 Announce Type: new Abstract: Standard model comparison is global, aggregating losses across the covariate space to declare a single winner.
arXiv Machine Learning
Jun 16
Amortized mean-shift interacting particles
arXiv:2606. 15871v1 Announce Type: cross Abstract: Bayesian inference for inverse problems is run to evaluate integrals -- posterior expectations, tail probabilities, and risks -- across a stream of observations.
Hugging Face Trending Papers
Aug 12
Small-Scale Experiments: Are We There Yet?
Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver. Instead, researchers have found them unreliable at small scales (starting at 4M parameters) and concluded that sizable models cannot be avoided.
arXiv Machine Learning
Jun 30
The Fundamental Limits of Valid Transport Map Estimation
arXiv:2606. 30574v1 Announce Type: new Abstract: Many modern generative modeling methods, including diffusion models, normalizing flows, and flow matching, estimate transport maps or plans between distributions without explicitly targeting an optimal transport (OT) map.
arXiv Machine Learning
Jul 21
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
arXiv:2605. 25811v2 Announce Type: replace-cross Abstract: We study counterfactual distribution learning for high-dimensional outcomes whose laws may concentrate near lower-dimensional structure.
arXiv AI
Jul 16
DeepLoop: Depth Scaling for Looped Transformers
arXiv:2607. 13491v1 Announce Type: cross Abstract: Looped Transformers scale sequential computation by applying a compact stack of physical blocks for multiple rounds, increasing unrolled depth without increasing stored parameters.
arXiv Machine Learning
Jun 3
Scalable Derivative Gaussian Processes via Exact Gradient Reduction
arXiv:2606. 02909v1 Announce Type: cross Abstract: Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations are expensive.
arXiv Machine Learning
Jun 2
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
arXiv:2605. 00941v4 Announce Type: replace Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem.
arXiv AI
Jul 3
$\mu$pscaling small models: Principled warm starts and hyperparameter transfer
arXiv:2602. 10545v2 Announce Type: replace-cross Abstract: Modern large-scale neural networks are often trained and released in multiple sizes to accommodate diverse inference budgets.
arXiv Machine Learning
Jun 25
Pre-Warm: Input-Conditioned Weight Initialization for Convolutional Neural Networks
arXiv:2606. 25256v1 Announce Type: cross Abstract: We introduce Pre-Warm, a simple yet effective zero-training-cost method for data-conditioned initialization of the first convolutional layer.