Neural Information Processing Systems http://nips.cc/

Although recent attempts to apply GNN to the FC network have shown promising results, there is still a common limitation that they usually do not incorporate the dynamic characteristics of the FC network which fluctuates over time.

Show an illustration, what does it do intuitively? Example of how to choose the wild bootstrap process: for instance, the statistical learning theory reader might wonder whether it makes sense to use a correlated Rademacher or Gaussian process here.

Neural Information Processing Systems http://nips.cc/

Although OOD detection methods have advanced by a great deal, they are still susceptible to adversarial examples, which is a violation of their purpose. To mitigate this issue, several defenses have recently been proposed. Nevertheless, these efforts remained ineffective, as their evaluations are based on either small perturbation sizes, or weak attacks.

In the past several years, it has become empirically clear that - contrary to traditional intuition - it is possible for models which exactly interpolate noisy training data to reliably generalize well on practical problems, especially in deep learning [7, 25, 34].

Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research efforts were devoted to the cases where data points are fitted with clusters, simplices, or general convex hulls. This paper aims to study nonconvex hull learning which has rarely been investigated in the literature. In order to learn data-adaptive nonconvex hulls, a novel approach is proposed based on a graph-theoretic measure that leverages graph cycles to characterize the structural complexities of input data points. Employing this measure, we present a greedy algorithmic framework, dubbed Zeta Hulls, to perform structured column sampling. The process of pursuing a Zeta hull involves the computation of matrix inverse. To accelerate the matrix inversion computation and reduce its space complexity as well, we exploit a low-rank approximation to the graph adjacency matrix by using an efficient anchor graph technique. Extensive experimental results show that data representation learned by Zeta Hulls can achieve state-of-the-art accuracy in text and image classification tasks.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper proposes a supervised learning algorithm. It uses stochastic gradient descent and periodically expands the hypothesis space by introducing new basis functions and adding corresponding components to the weight vector. As such, as it processes more data, it fits more complex models. The hypothesis space considered here are polynomials and higher order monomials are gradually introduced to the model. The concept of growing the hypothesis space as more data is introduced is not new (training kernel methods with SGD exhibits this behavior), but in the proposed method, choosing which monomials to add to the hypothesis space is very cheap.