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LIRA (Loss-Informed Rank Allocation) is an online rank allocation method based on loss perturbation, aimed at making the rank reallocation process interpretable item by item.
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11/4/2026
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UnverifiedNetwork rank refers to the intermediate dimension of the low-rank decomposition matrices and determines the feature capacity LoRA can express; too small a rank limits the ability to learn complex concepts, while too large a rank increases the risk of overfitting and the adapter size.62% similarUnverifiedDomain shift refers to the performance degradation that occurs when the distribution of the model's training data is inconsistent with that of the deployment data.62% similarUnverifiedCross-entropy loss is the most commonly used loss function for classification tasks; rooted in information theory, it has gradient properties better suited to classification problems than MSE.61% similarUnverifiedIn online episodic tabular MDPs, the loss function is adversarial, the environment can choose an arbitrary loss at each round, and the feedback is bandit-style, i.e., only the loss of the chosen action can be observed.61% similarUnverifiedCommonly used loss functions for contrastive learning include InfoNCE Loss and Triplet Loss.60% similar
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