In this paper, we address the online learning problem when the input to the greedy algorithm is stochastic with unknown parameters that have to be learned over time.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

This problem involves identifying a density function that accurately represents the distribution of a given dataset.

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 layerwise sparsities, leading to performance degradation in challenging tasks.

Open-vocabulary 3D object detection (OV-3Det) aims to generalize beyond the limited number ofbasecategories labeled during thetraining phase. Thebiggest bottleneck is the scarcity of annotated 3D data, whereas 2D image datasets are abundantandrichlyannotated.