Zitong Yang

Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan et al.

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e.g. a neural network) by fitting the model's residuals with a $k$-nearest neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. Empirically, we show that ResMem consistently improves the test set generalization of the original prediction model across various standard vision and natural language processing benchmarks. Theoretically, we formulate a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over the base predictor.

Yaodong Yu, Zitong Yang, Alexander Wei et al.

We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model predictions to pseudo-label test samples and then trains a new model on the pseudo-labels. The more the new model's parameters differ from an in-distribution model, the greater the predicted OOD error. Empirically, our approach outperforms existing methods on both image and text classification tasks and across different network architectures. Theoretically, we connect our approach to a bound on the test error for overparameterized linear models. Furthermore, we find that Projection Norm is the only approach that achieves non-trivial detection performance on adversarial examples. Our code is available at https://github.com/yaodongyu/ProjNorm.

Naibo Wang, Zhiling Luo, Xiya Lyu et al.

Web services are widely used in many areas via callable APIs, however, data are not always available in this way. We always need to get some data from web pages whose structure is not in order. Many developers use web data extraction methods to generate wrappers to get useful contents from websites and convert them into well-structured files. These methods, however, are designed specifically for professional wrapper program developers and not friendly to users without expertise in this domain. In this work, we construct a service wrapper system to convert available data in web pages into web services. Additionally, a set of algorithms are introduced to solve problems in the whole conversion process. People can use our system to convert web data into web services with fool-style operations and invoke these services by one simple step, which greatly expands the use of web data. Our cases show the ease of use, high availability, and stability of our system.