Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
The road less scheduled.arXiv [cs.LG]
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
CoAction applies a transformer encoder with per-task embeddings to jointly solve multiple multi-objective optimization problems by capturing cross-task correlations.
HDET lets data-parallel replicas explore a spread of learning rates independently before averaging parameters, with an auto-LR controller driven by inter-replica loss differences to produce a self-adapting schedule without extra sweeps.
Pretraining plus Mixup/TrivialAugment and a feature pyramid network lift macro-F1 from 0.65 to 0.69 on 43-class malware image classification while cutting training epochs from 96 to 10.
citing papers explorer
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From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models
Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
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CoAction: Cross-task Correlation-aware Pareto Set Learning
CoAction applies a transformer encoder with per-task embeddings to jointly solve multiple multi-objective optimization problems by capturing cross-task correlations.
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Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models
HDET lets data-parallel replicas explore a spread of learning rates independently before averaging parameters, with an auto-LR controller driven by inter-replica loss differences to produce a self-adapting schedule without extra sweeps.
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Image-Based Malware Type Classification on MalNet-Image Tiny: Effects of Multi-Scale Fusion, Transfer Learning, Data Augmentation, and Schedule-Free Optimization
Pretraining plus Mixup/TrivialAugment and a feature pyramid network lift macro-F1 from 0.65 to 0.69 on 43-class malware image classification while cutting training epochs from 96 to 10.