PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
arXiv preprint arXiv:2508.15008 (2025)
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CNN models with attention reach 99.05% top-1 accuracy on line-level splits and 78.61% on page-disjoint splits for writer identification after expanding the labeled portion of the Muharaf historical Arabic manuscript dataset.
Aggressive compression of recursive reasoners keeps local predictions intact but destroys global reasoning accuracy, recoverable with calibrated INT4 and detectable via carry-trajectory fidelity.
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.
citing papers explorer
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Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization
PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
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Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
CNN models with attention reach 99.05% top-1 accuracy on line-level splits and 78.61% on page-disjoint splits for writer identification after expanding the labeled portion of the Muharaf historical Arabic manuscript dataset.
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What Survives When You Compress a Recursive Reasoner for the Edge?
Aggressive compression of recursive reasoners keeps local predictions intact but destroys global reasoning accuracy, recoverable with calibrated INT4 and detectable via carry-trajectory fidelity.
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TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference
Tiny NeRV models using capacity scaling, frequency-aware distillation, and low-precision quantization achieve favorable quality-efficiency trade-offs with far fewer parameters and lower computational costs than standard NeRV.