Introduces a method to design structure-specific relational inductive biases for a base transformer architecture, enabling end-to-end transcription of documents with intrinsic structures, demonstrated on sheet music, shape drawings, and mechanical engineering drawings.
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author Weiss, Y
16 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.
Defines recoverability maps via dense synthetic degradation sweeps and two summary metrics to show AI restoration recovers license plates from ~93% of extreme angle parameter space, with geometry rather than model architecture as the binding limit.
Nonlinear dimensionality reduction on ECG signals enables unsupervised personalized arrhythmia detection with high accuracy on 2D embeddings using standard algorithms on the MIT-BIH database.
FSDC-DETR improves small-object AP by 6.8–6.9 points on VisDrone and AITODv2 by explicit frequency-spatial fusion and wavelet-style downsampling inside a DETR hybrid encoder.
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.
A two-stage OMR pipeline decodes symbol candidates into polyphonic score structures via topology recognition with probability-guided search.
MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.
Pretraining on broad sound events plus on-the-fly augmentations improves out-of-domain true-positive rates for acoustic drone detection at fixed low false-positive rates.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
Applies consistency bi-directional GAN to image representations of raw PE and OLE binaries for anomaly detection, reporting stable AUC across datasets including 214 malware families.
A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future directions.
citing papers explorer
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A document is worth a structured record: Principled inductive bias design for document recognition
Introduces a method to design structure-specific relational inductive biases for a base transformer architecture, enabling end-to-end transcription of documents with intrinsic structures, demonstrated on sheet music, shape drawings, and mechanical engineering drawings.
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Routine Computing: A Systematic Review of Sensing Daily Life Dimensions Towards Human-Centered Goals
The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
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Generating Financial Time Series by Matching Random Convolutional Features
Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.
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Mapping License Plate Recoverability Under Extreme Viewing Angles for Opportunistic Urban Sensing
Defines recoverability maps via dense synthetic degradation sweeps and two summary metrics to show AI restoration recovers license plates from ~93% of extreme angle parameter space, with geometry rather than model architecture as the binding limit.
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Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
Nonlinear dimensionality reduction on ECG signals enables unsupervised personalized arrhythmia detection with high accuracy on 2D embeddings using standard algorithms on the MIT-BIH database.
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FSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small Object Detection
FSDC-DETR improves small-object AP by 6.8–6.9 points on VisDrone and AITODv2 by explicit frequency-spatial fusion and wavelet-style downsampling inside a DETR hybrid encoder.
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TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
TaskFusion combines AGF feature mapping, cross-task augmentation, and distilled replay for continual anomaly detection on heterogeneous tabular data, reporting gains over baselines on 21 datasets.
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From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR
A two-stage OMR pipeline decodes symbol candidates into polyphonic score structures via topology recognition with probability-guided search.
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Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.
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Improving acoustic drone detection generalization through pretraining and data augmentation
Pretraining on broad sound events plus on-the-fly augmentations improves out-of-domain true-positive rates for acoustic drone detection at fixed low false-positive rates.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
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Enhanced Consistency Bi-directional GAN (CBiGAN) for Malware Anomaly Detection
Applies consistency bi-directional GAN to image representations of raw PE and OLE binaries for anomaly detection, reporting stable AUC across datasets including 214 malware families.
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Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future directions.
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