Quantitative Bayesian inference using a deep-learning emulator detects 0.018-0.020 M_sun of helium in the Type Ic supernova 2014L.
Mixed citations
Deep residual learning for image recognition,
Mixed citation behavior. Most common role is method (46%).
126
Pith papers citing it
164.2k
external citations · Crossref
Method
46% of classified citations
citation-role summary
method 18
background 14
baseline 2
dataset 1
citation-polarity summary
representative citing papers
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.
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.
MorphoHELM is a new benchmark for Cell Painting morphology representations that tests methods across increasing batch effect levels and finds classic computer vision strategies remain the strongest general-purpose performers.
VCR learns valid contextual representations for incomplete wearable signals via orthogonal disentanglement and missing-aware mixture-of-experts, improving robustness across full and missing-modality settings.
The paper develops a martingale-consistent SSL framework enforcing expected coherence between coarse and refined predictions via new objectives and a Monte Carlo estimator, improving robustness under partial observations.
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.
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
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.
Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
Trust-SSL introduces additive-residual trust weights in SSL to selectively handle corruptions in aerial imagery, yielding higher linear-probe accuracy and larger gains under severe degradations than SimCLR or VICReg.
A shared mixed-activation network of width 2dN+d+2 yields layer-wise L^p approximation rates bounded by the modulus of continuity at geometric scale N^{-ℓ}, reducing to (2d+1)N^{-ℓ} for 1-Lipschitz targets.
FRTSearch reframes fast radio transient detection as instance segmentation on dynamic spectra and uses the segmented shapes to infer dispersion measure and time of arrival, achieving 98% recall with over 99.9% fewer false positives than traditional methods.
CapBench is a new multi-PDK dataset of post-layout 3D windows with high-fidelity capacitance labels and multiple ML-ready representations, plus baseline results showing CNN accuracy versus GNN speed trade-offs.
Mosaic uses cross-modal clusters as the unit for KVCache organization in VLMs to achieve up to 1.38x speedup in streaming long-video understanding.
SMCNet applies a complex-valued CNN to mmWave radar IQ data for high-accuracy surface material classification across multiple and unseen sensing distances.
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
SecurePix uses FeFET multidomain polarization states for in-pixel symmetric-key encryption, dropping ResNet-18 accuracy to 9.58% on MNIST and 6.98% on CIFAR-10 while supporting key-based decryption via lookup table.
S2-WEF detects dynamic free-riders in federated learning by simulating attack WEF patterns from prior global models, combining them with mutual deviation scores, and using two-dimensional clustering without proxy data or pre-training.
Fusion2Print fuses flash-non-flash contactless fingerprints via attention-based networks and U-Net enhancement to reach AUC 0.999 and EER 1.12% with cross-domain compatibility.
EMP maps importance scores to effective sample size N_eff and prunes the lowest N - N_eff components, with a derived lower bound on retained effective mass and upper bound on loss increase.
citing papers explorer
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Traces of Helium Detected in Type Ic Supernova 2014L
Quantitative Bayesian inference using a deep-learning emulator detects 0.018-0.020 M_sun of helium in the Type Ic supernova 2014L.
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SDM: A Powerful Tool for Evaluating Model Robustness
SDM is a new staged gradient attack that reconstructs the adversarial objective around probability differences and reports stronger performance than prior methods like APGD.
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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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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
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.
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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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MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays
MorphoHELM is a new benchmark for Cell Painting morphology representations that tests methods across increasing batch effect levels and finds classic computer vision strategies remain the strongest general-purpose performers.
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VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
VCR learns valid contextual representations for incomplete wearable signals via orthogonal disentanglement and missing-aware mixture-of-experts, improving robustness across full and missing-modality settings.
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Martingale-Consistent Self-Supervised Learning
The paper develops a martingale-consistent SSL framework enforcing expected coherence between coarse and refined predictions via new objectives and a Monte Carlo estimator, improving robustness under partial observations.
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Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception
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.
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GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
GPROF-IR is a CNN-based retrieval that uses temporal context in geostationary IR observations to produce precipitation estimates with lower error than prior IR methods and climatological consistency with PMW retrievals for integration into IMERG V08.
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Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
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GEODE: Angle-Adaptive OOD Detection with Universal Scorer Compatibility
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.
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PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
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Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Trust-SSL introduces additive-residual trust weights in SSL to selectively handle corruptions in aerial imagery, yielding higher linear-probe accuracy and larger gains under severe degradations than SimCLR or VICReg.
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Geometric Layer-wise Approximation Rates for Deep Networks
A shared mixed-activation network of width 2dN+d+2 yields layer-wise L^p approximation rates bounded by the modulus of continuity at geometric scale N^{-ℓ}, reducing to (2d+1)N^{-ℓ} for 1-Lipschitz targets.
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FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation
FRTSearch reframes fast radio transient detection as instance segmentation on dynamic spectra and uses the segmented shapes to infer dispersion measure and time of arrival, achieving 98% recall with over 99.9% fewer false positives than traditional methods.
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CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction
CapBench is a new multi-PDK dataset of post-layout 3D windows with high-fidelity capacitance labels and multiple ML-ready representations, plus baseline results showing CNN accuracy versus GNN speed trade-offs.
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Mosaic: Cross-Modal Clustering for Efficient Video Understanding
Mosaic uses cross-modal clusters as the unit for KVCache organization in VLMs to achieve up to 1.38x speedup in streaming long-video understanding.
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SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs
SMCNet applies a complex-valued CNN to mmWave radar IQ data for high-accuracy surface material classification across multiple and unseen sensing distances.
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Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
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Lightweight True In-Pixel Encryption with FeFET Enabled Pixel Design for Secure Imaging
SecurePix uses FeFET multidomain polarization states for in-pixel symmetric-key encryption, dropping ResNet-18 accuracy to 9.58% on MNIST and 6.98% on CIFAR-10 while supporting key-based decryption via lookup table.
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Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns
S2-WEF detects dynamic free-riders in federated learning by simulating attack WEF patterns from prior global models, combining them with mutual deviation scores, and using two-dimensional clustering without proxy data or pre-training.
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Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching
Fusion2Print fuses flash-non-flash contactless fingerprints via attention-based networks and U-Net enhancement to reach AUC 0.999 and EER 1.12% with cross-domain compatibility.
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Effective Model Pruning: Measure The Redundancy of Model Components
EMP maps importance scores to effective sample size N_eff and prunes the lowest N - N_eff components, with a derived lower bound on retained effective mass and upper bound on loss increase.
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Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios
RITA models image manipulation localization as ordered sequence prediction with a new benchmark HSIM and HSS metric to handle multi-step editing processes.
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MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness
MAGIC is a few-shot mask-guided anomaly inpainting framework using Gaussian prompt perturbation, spatially adaptive guidance, and context-aware mask alignment to produce high-fidelity, diverse anomalies that outperform prior methods on downstream detection tasks.
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Language Models as Knowledge Bases?
BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
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Ultra-High-Definition Image Quality Assessment via Graph Representation Learning
UHD-GCN-BIQA models structural dependencies among sampled patches via a hybrid kNN graph and residual graph convolutions to achieve competitive PLCC and SRCC with the lowest RMSE on the UHD-IQA benchmark for blind ultra-high-definition image quality assessment.
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VBT-MPC: Vision-Based Tactile MPC for Contour Following
VBT-MPC performs robotic contour following by running MPC directly in vision-based tactile contour feature space and is tested on varied geometries in simulation and real experiments.
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Chessformer: A Unified Architecture for Chess Modeling
Chessformer is a unified encoder-only transformer for chess that uses square tokens, geometric attention bias, and an attention-based policy head to set new records in human move prediction accuracy, playing strength, and interpretability.
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Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
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Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex
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.
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Venus-DeFakerOne: Unified Fake Image Detection & Localization
DeFakerOne integrates InternVL2 and SAM2 into a single model that achieves state-of-the-art results on 39 detection and 9 localization benchmarks for unified fake image detection and localization.
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Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
Adam-SHANG is a convergent Adam variant for stochastic smooth convex optimization that uses a stable lagged-preconditioner update and a computable trace-ratio stepsize rule.
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OUIDecay: Adaptive Layer-wise Weight Decay for CNNs Using Online Activation Patterns
OUIDecay adaptively rescales layer-wise weight decay in CNNs using an online activation-based Overfitting-Underfitting Indicator and outperforms fixed decay in 7 of 8 tested settings.
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Hybrid Machine Learning and Physical Modeling of Feedstock Deformation During Robotic 3D Printing of Continuous Fiber Thermoplastic Composites
A hybrid Kelvin-Voigt viscoelastic and stabilized neural ODE model, identified from DMA and DSC experiments, predicts composite prepreg deformation in robotic 3D printing and generalizes beyond training temperatures.
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Gradient-Discrepancy Acquisition for Pool-Based Active Learning
Introduces gradient-discrepancy acquisition criterion derived from Luo et al. (2022) generalization bound for active learning.
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MooD: Perception-Enhanced Efficient Affective Image Editing via Continuous Valence-Arousal Modeling
MooD introduces continuous valence-arousal modeling with VA-aware retrieval and perception-enhanced guidance for efficient, controllable affective image editing, plus a new AffectSet dataset.
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Model Merging: Foundations and Algorithms
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
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PrismAgent: Illuminating Harm in Memes via a Zero-Shot Interpretable Multi-Agent Framework
PrismAgent deploys four specialized LLM agents in sequence to analyze meme intent, gather context, make preliminary judgments, and deliver a final harm verdict, outperforming prior zero-shot methods on three public datasets.
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Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts
A broad empirical benchmark shows how 15 existing test selection metrics perform for fault detection, performance estimation, and retraining under corrupted, adversarial, temporal, natural, and label shifts across image, text, and Android data.
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CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans
CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
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Physics-Informed Tracking (PIT)
PIT uses a neural autoencoder with a differentiable physics module and a new Physics-Informed Landmark Loss to track single particles in video, achieving sub-pixel accuracy in supervised and unsupervised modes.
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Co-Design of CNN Accelerators for TinyML using Approximate Matrix Decomposition
A co-design framework using approximate matrix decomposition and genetic algorithms delivers 33% average latency reduction in TinyML CNN FPGA accelerators with 1.3% average accuracy loss versus standard systolic arrays.
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Generative Modeling of Complex-Valued Brain MRI Data
A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
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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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LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators
Simulations indicate that 2500 LSST strong lenses can exclude 74% and 36% of the prior volume on halo mass function parameters at 3σ and 5σ, with sensitivity from both high- and low-mass halos plus line-of-sight contributions.
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Extraction of linearized models from pre-trained networks via knowledge distillation
Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
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Variational Feature Compression for Model-Specific Representations
A variational latent bottleneck with KL regularization and a dynamic binary mask based on saliency produces model-specific features that keep high accuracy for one classifier but drop others below 2% on CIFAR-100 with over 45x suppression.
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Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition
UFPR-VeSV is a new real-world dataset for fine-grained vehicle classification and automatic license plate recognition collected from Brazilian police cameras, with benchmarks demonstrating its difficulty and the value of joint task use.