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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MM-Eval unifies evaluation of multimodal summaries by integrating factual text quality, cross-modal relevance via MLLM judge, and visual diversity via truncated CLIP entropy, then calibrates their combination on human preferences.
MIRL uses mutual information to guide trajectory selection and provide separate rewards for visual perception in RLVR for VLMs, achieving 70.22% average accuracy with 25% fewer full trajectories.
LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
SPeCTrA-Sum uses hierarchical cross-modal fusion via DVP and DPP-distilled image selection via VRP to generate more accurate and visually grounded multimodal summaries.
ViSA-R2 recovers single executable SymPy expressions for linear steady-state fields from visualizations using a self-verifying chain-of-thought that recognizes patterns, hypothesizes solution families, derives parameters, and checks consistency.
VCON is a unified framework for smooth iterative DNN compression that uses parallel execution and an affine combination to progressively replace the original model with its compressed form during fine-tuning.
KLR Hopfield networks exhibit robustness to quantization but sensitivity to pruning, interpreted as arising from dense bimodal parameterization of sparse input mappings.
Gated-SwinRMT unifies Swin windowed attention with retentive Manhattan decay via gating, reaching 80.22% top-1 accuracy on Mini-ImageNet versus 73.74% for the RMT baseline.
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.
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Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.