In the proportional high-dimensional regime, stronger backdoor training triggers improve clean accuracy and make attack success non-monotonic for regularized GLMs on Gaussian mixtures, with closed-form proofs for squared loss and fixed-point extensions to convex losses.
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Learning multiple layers of features from tiny images
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Data geometry makes time identifiable from noisy interpolants at rate O(1/sqrt(d-k)), rendering the time-blindness gap asymptotically negligible relative to coupling variance.
p-MEM stores distribution parameters and samples at native memory bandwidth, achieving over 1000 GSa/s/mm² GRNG throughput and reducing sampling latency and energy in probabilistic edge workloads.
Introduces Label-NTK and Residual-NTK alignments to derive tighter NTK convergence bounds that track the full eigen-spectrum and match observed training speed.
Introduces complement-aware submodular functions (CSI) that preserve structure between subset and complement for improved robust data selection.
ASR applies per-channel variance-matching corrections stabilized by data-driven shrinkage to recover accuracy in highly sparse convolutional networks without retraining.
FeatCal reduces feature drift in merged models via layer-wise closed-form calibration on a small dataset, outperforming prior post-merging methods on CLIP and GLUE benchmarks with high sample efficiency.
Fitting logic gates as 4D multilinear polynomials with covariance Jacobian selection matches or beats 16D softmax baselines on seven datasets and remains stable at 12-layer depth where the baseline drops 37 points on CIFAR-10.
Online kernel regression equals offline regression with shifted targets; correcting the targets lets online learning match offline performance and outperform true targets in continual image classification.
DR-IS selects low-contamination subsets via bounded rank-disagreement in proxy ensembles under an ε-contamination model, with O(√(log(N/δ)/K)) concentration rates that certify separation when the expectation gap Δ' is positive.
GEODE uses per-sample cosine-similarity scaling in a norm loss to preserve feature geometry for universal scorer-compatible OOD detection, matching or exceeding OE performance on CIFAR benchmarks.
WONN is a new oscillatory neural network based on generalized Winfree dynamics that scales competitively to ImageNet-1K and reaches 80.1% accuracy on Maze-hard with 1% of prior model parameters.
EIHF injects high-frequency evidence early to reshape class-conditional feature geometry and reduce ID/OOD Mahalanobis overlap for geometry-sensitive OOD detection.
TIDE is a neuro-inspired architecture using stabilized asymmetric E-I networks with lateral inhibition and 80:20 balance that trains in under half the time of CTM while gaining +1.65% top-1 accuracy on perturbed ImageNet.
MINE uses mechanistic interpretability on language-aligned image representations to generate per-voxel feature descriptions, validated via image generation and counterfactual edits that causally shift brain activation.
Target-aligned data selection via normalized endpoint loss drop on a validation-induced reference path achieves competitive performance with reduced computational overhead.
Contextual Plackett-Luce extends the classical Plackett-Luce model with context-dependent Ising parameterization to enable efficient parallel scoring followed by incremental autoregressive selection for ambiguous sequence tasks.
VISTA adaptively tunes consistency thresholds in decentralized SGD so that the system converges asymptotically like standard SGD even when adversaries dominate the worker pool.
New optimizer uses auxiliary loss to imitate low-order Hessian information, replacing gradient squares in Adam-like training with convergence guarantee and some experimental gains.
Hierarchy-Aware Cross-Entropy improves image classification by incorporating class hierarchies into the loss through prediction aggregation and ancestral label smoothing, achieving mean accuracy gains of 4.66% in end-to-end training and 2.18% in linear probing.
SignMuon merges majority-vote sign aggregation from signSGD with Muon's polar-factor steps to create a communication-efficient distributed optimizer that matches signSGD rates under symmetric noise and shows strong empirical results on CIFAR and nanoGPT.
DR-SNE augments the SNE objective with a density regularization term from normalized log-density estimates to preserve relative densities while retaining neighborhood structure.
LightSplit uses non-invertible orthogonal projections as an information bottleneck in split learning to reduce transmitted dimensionality by 32x while retaining more than 95% accuracy and limiting reconstruction risk.
Standard unlearning metrics disagree in multimodal settings, but a correlation-weighted Unified Quality Score delivers consistent method rankings across benchmarks.
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What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
Data geometry makes time identifiable from noisy interpolants at rate O(1/sqrt(d-k)), rendering the time-blindness gap asymptotically negligible relative to coupling variance.