We address the problem of denoising data from a Gaussian mixture using a two-layer non-linear autoencoder with tied weights and a skip connection.

When arenoisy, this =1 inEg (called over -fitting tothetruetar24, 31, 32, 33, 34, 35, 36, 37, 38, 39, 16].

We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power.

Unfortunately, many real-world problems of practical interest have these characteristics.

The knowledge seeking procedure described in Section 2.1 applies a search algorithm over the graph Each of such queries takes constant time. As mentioned in Section 2.3, the approach described in this paper can be used to answer any valid We proceed by induction on the number of literals |Q |. 3 Base case. For the experiments on KBQA, we assume that we only have access to pairs of questions and answers, i.e. the actual inferential chain leading from the question to the answer is latent. Therefore, we resort to weak supervision to train the model. Inspired by such insight, we employ a similar technique to enhance the performance of our model.

Therearetwoupdatingstrategies: 1) mimicking strategy to generate similar samples based on original data, preserving stylistic and contextual essence, and 2) extending strategy that further expands existing samples at varying cognitive levels by adapting Bloom's taxonomy ofeducational objectives. Extensiveexperiments onupdated MMLU andBIG-Bench demonstrate thestability oftheproposed strategiesandfindthat the mimicking strategy can effectively alleviate issues of overestimation from benchmark leakage. In cases where the efficient mimicking strategy fails, our extending strategystill showspromising results.