Recently, a series of studies have tried to extract interactions between input variables
modeled by a DNN and define such interactions as concepts encoded by the DNN …

This paper theoretically explains the intuition that simple concepts are more likely to be
learned by deep neural networks (DNNs) than complex concepts. In fact, recent studies …

This paper aims to illustrate the concept-emerging phenomenon in a trained DNN.
Specifically, we find that the inference score of a DNN can be disentangled into the effects of …

Adversarial Defense of deep neural networks have gained significant attention and there
have been active research efforts on model vulnerabilities for attacking such as gradient …

The efficient and theoretically sound uncertainty quantification is crucial for building trust in
deep learning models. This has spurred a growing interest in conformal prediction (CP), a …

This study proves the two-phase dynamics of a deep neural network (DNN) learning
interactions. Despite the long disappointing view of the faithfulness of post-hoc explanation …

Masking some input variables of a deep neural network (DNN) and computing output
changes on the masked input sample represent a typical way to compute attributions of input …

This study aims to prove the emergence of symbolic concepts (or more precisely, sparse
primitive inference patterns) in well-trained deep neural networks (DNNs). Specifically, we …

In this paper, we explain the inference logic of large language models (LLMs) as a set of
symbolic concepts. Many recent studies have discovered that traditional DNNs usually …

Pre-training has exhibited notable benefits to downstream tasks by boosting accuracy and
speeding up convergence, but the exact reasons for these benefits still remain unclear. To …