Therefore, it is necessary to perform trajectory prediction based on instantaneous observations, which has rarely been studied before.

With the top-ranked PTM candidates, we further learn to enrich task repr.

Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections.

NER learns to regress continuous representations of neural dynamics (i.e., embeddings) on continuous movements.

Second, we enforce the posterior update model to learn the dynamics of the latent variable.

In this paper, we present DSA, the first automated framework for discovering sparsity allocation schemes for layer-wise pruning in Large Language Models (LLMs). LLMs have become increasingly powerful, but their large parameter counts make them computationally expensive. Existing pruning methods for compressing LLMs primarily focus on evaluating redundancies and removing element-wise weights. However, these methods fail to allocate adaptive layer-wise sparsities, leading to performance degradation in challenging tasks.

Videos of the robot are best viewed at baku-robot.github.io.

Experiments show that training on the high-quality data selected by our method can often outperform other data selection methods for collaborative fine-tuning of LLMs, across diverse private domain datasets, in medical, multilingual and financial settings.