This paper discusses the potential of utilizing the known physical laws to improve the machine learning model generalizability for AIoT applications. Through three case studies, we demonstrate that physics-informed machine learning can (1) effectively assist the generalization of deep neural networks and...
Despite decades of development by researchers, anomaly-based NIDS are only rarely employed in real-world applications, most possibly due to the lack of generalization power of the proposed models. This article first evaluates four unsupervised machine learning methods on two recent datasets and then ...
Although these models perform well in classifying majority-class samples, their generalization ability on minority-class samples could be improved. Improving the representation learning and generalization capabilities of the model has become a key research direction in the field of text classification, and...
6.5 Data annotation and generalization Most VSS applications still face severe labeling problems where the intractability to collect new large annotated amounts of data (including plentiful images with large diversity) is perceived. In this regard, learning generalized models is an actual problem that ...
In response to real-world scenarios, the domain generalization (DG) problem has spurred considerable research in person re-identification (ReID). This chal
In particular, model regularization is a fundamental technique for improving the generalization performance of a predictive model. Accordingly, many efficient optimization algorithms have been developed for solving various machine learning formulations with different regularizations. In this study, we focus ...
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks Tianlong Wang, Junzhe Chen, Xueting Han, Jing Bai RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar ...
As always, good generalization is the central goal in forecasting applications. 1.4. Auto association Auto-association models are those in which we use auto-associative neural networks to learn a compressed or reduced representation of the input data. ...
Adding constraints on the size of the regression coefficients matrix W (known in different communities as Tikhonov regularization, ridge regression, weight decay) or the sparsity of said matrix (Lasso: Tibshirani, 1996) can be seen as ways of improving the generalization properties of the estimator...
A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is...