Asia
Learning A Structured Optimal Bipartite Graph for Co-Clustering
Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang
Ingraph based co-clustering methods, abipartite graph isconstructed todepict the relation between features and samples.
On model selection consistency of penalized M-estimators: a geometric theory
Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Neural Information Processing Systems http://nips.cc/
Stochastic Online Greedy Learning with Semi-bandit Feedbacks
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.
Conditional Generative Moment-Matching Networks
Yong Ren, Jun Zhu, Jialian Li, Yucen Luo
Neural Information Processing Systems http://nips.cc/
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
Neural Information Processing Systems http://nips.cc/
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
Zheng Qu, Peter Richtarik, Tong Zhang
Neural Information Processing Systems http://nips.cc/
OptimalHypothesisSelectionin(Almost) LinearTime
This problem involves identifying a density function that accurately represents the distribution of a given dataset.
DiscoveringSparsityAllocationforLayer-wise PruningofLargeLanguageModels
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.