FiberTune is a new fine-tuning objective that preserves action-fiber visual residuals in VLA policies, yielding performance gains on simulation and physical robot tasks.
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4 Pith papers cite this work, alongside 3,211 external citations. Polarity classification is still indexing.
4
Pith papers citing it
3,211
external citations · OpenAlex
years
2026 4verdicts
UNVERDICTED 4representative citing papers
SIVR detects LLM hallucinations by learning from token-wise and layer-wise variance patterns in internal hidden states, outperforming baselines with better generalization and less training data.
Empirical tests on 148 images show that the best colorspace for k-means quantization depends on the image and the target number of colors k, with RGB winning in roughly half the cases.
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
citing papers explorer
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FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning
FiberTune is a new fine-tuning objective that preserves action-fiber visual residuals in VLA policies, yielding performance gains on simulation and physical robot tasks.
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Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
SIVR detects LLM hallucinations by learning from token-wise and layer-wise variance patterns in internal hidden states, outperforming baselines with better generalization and less training data.
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Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces
Empirical tests on 148 images show that the best colorspace for k-means quantization depends on the image and the target number of colors k, with RGB winning in roughly half the cases.
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Explaining Machine Learning and Memorization with Statistical Mechanics
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.