The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to either class. Instead of plug-in approaches, we propose a support vector classifier to construct confidence sets in a flexible manner. Theoretically, we show that the proposed learner can control the non-coverage rates and minimize the ambiguity with high probability. Efficient algorithms are developed and numerical studies illustrate the effectiveness of the proposed method.

Neuron interpretation offers valuable insights into how knowledge is structured within a deep neural network model.

The warping estimation module W is based on an hourglass with five conv3 3 - bn - relu - pool2 2 in the encoders and five upsample2 2 - conv3 3 - bn - relu blocks in the decoders. In G, we use the Johnson architecture [ 3 ] with two down-sampling blocks, six residual-blocks and two up-sampling blocks. The design follows [ 7 ]. The inputs are the base image, displacement field, and inpainting map. It downsampled 4 and upsampled 4 to get the output, i.e. the reconstructed image.

By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks.

A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility.

Examples include early diagnosis of medical diseases [22], threat detection for cyber-attacks [3, 18, 36], or safety analysis for self-driving cars [38]. Manyreal-world anomalies can be detected intime series data.

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