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Found 6 papers, 0 papers with code
A Precise Characterization of SGD Stability Using Loss Surface Geometry
In this paper, we delve deeper into the relationship between linear stability and sharpness.
QuantEase: Optimization-based Quantization for Language Models
Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets.
Isometric Graph Neural Networks
Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph.
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors.
