TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
StableTTA improves ImageNet-1K accuracy across 71 vision models by stabilizing logit aggregation under coherent-batch inference and enabling efficient single-forward-pass adaptation.
CoLLM-NAS introduces a collaborative two-LLM framework with Navigator, Generator, and Coordinator modules to perform knowledge-guided neural architecture search, reporting state-of-the-art results on ImageNet and NAS-Bench-201 with 4-10x lower search cost.
citing papers explorer
-
TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles
TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
-
StableTTA: Improving Vision Model Performance by Training-free Test-Time Adaptation Methods
StableTTA improves ImageNet-1K accuracy across 71 vision models by stabilizing logit aggregation under coherent-batch inference and enabling efficient single-forward-pass adaptation.
-
CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search
CoLLM-NAS introduces a collaborative two-LLM framework with Navigator, Generator, and Coordinator modules to perform knowledge-guided neural architecture search, reporting state-of-the-art results on ImageNet and NAS-Bench-201 with 4-10x lower search cost.