Interpretable Rotation-Equivariant Multiary-Valued Network for Attribute Obfuscation.

This paper focuses on the problem of preventing information leakage in neural networks, i.e., assuming that attackers have obtained intermediate-layer features of a neural network, and preventing attackers from inverting these features to the input with private information.

Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions.

Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output. However, existing attribution methods are often built upon different heuristics. There remains a lack of a unified theoretical understanding of why these methods are effective and how they are related.

Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing.

This study proposes a set of generic rules to revise existing neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs), in order to make feature representations of neural networks to be rotation-equivariant and permutation-invariant. Rotation equivariance of features means that the feature computed on a rotated input point cloud is the same as applying the same rotation transformation to the feature computed on the original input point cloud. We find that the rotation-equivariance of features is naturally satisfied, if a neural network uses quaternion features.

Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans.

Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C.

Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification.

Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. In this paper, we provide a new perspective to explain the success of knowledge distillation based on the information theory, i.e.

Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering.

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.

Extraction of an Explanatory Graph to Interpret a CNN.

This paper introduces an explanatory graph representation to reveal object parts encoded inside convolutional layers of a CNN. Given a pre-trained CNN, each filter in a conv-layer usually represents a mixture of object parts. We develop a simple yet effective method to learn an explanatory graph, which automatically disentangles object parts from each filter without any part annotations.

Interpretable CNNs for Object Classification.

This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervision. Instead, we use the same training data as traditional CNNs.

Object Discovery: Soft Attributed Graph Mining.

We categorize this research in terms of its contribution to both graph theory and computer vision. From the theoretical perspective, this study can be considered as the first attempt to formulate the idea of mining maximal frequent subgraphs in the challenging domain of messy visual data, and as a conceptual extension to the unsupervised learning of graph matching. We define a soft attributed pattern (SAP) to represent the common subgraph pattern among a set of attributed relational graphs (ARGs), considering both their structure and attributes.

Protoporphyrin IX catalyzed hydrogen peroxide to generate singlet oxygen.

Aim: To study the role of protoporphyrin IX (pPIX) in mitochondrial metabolism of hydrogen peroxide (H2O2).

Methods: O2 (-) specific fluorescent markers DMA (9,10-dimerthylanthracence) and SOSG (Singlet Oxygen Sensor Green reagent) were used for measurement of singlet oxygen ((1)O2). Catalyzing conversion of H2O2 into (1)O2 by pPIX was monitored in vitro under varied H2O2 content, temperature, and PH value in the reaction.