Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks.

However, these methods typically reach superlinear convergence only when the stochastic Hessian noise diminishes, increasing per-iteration costs over time.

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Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs.

We establish a mathematical framework for guided diffusion to systematically study its optimization theory and algorithmic design.

Contrastive Clustering loss and update the prototypical vectors.Algorithm 1: Prototypical Constrative Clustering Loss Compute Prototypical Constrative Clustering Loss based on Eqn. Compute Prototypical Constrative Clustering Loss based on Eqn. Two segmentation network architectures have been used in our experiments, i.e., (1) DeepLab-V3 The learning rate is set individually for each step and dataset. Similarly, to illustrate the effectiveness and robustness of our method in the non-incremental setting. We also perform an additional ablation study on the ADE20K (100-50) benchmark to investigate the impact of the delta.

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes.

The majority of language model training builds on imitation learning.