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05/03/2023
Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models
This paper aims to prove the emergence of symbolic concepts in well-trai...
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04/26/2023
Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs
In this technical note, we aim to explain a deep neural network (DNN) by...
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04/04/2023
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
The Shapley value is widely regarded as a trustworthy attribution metric...
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04/03/2023
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
In this paper, we explain the inference logic of large language models (...
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03/02/2023
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Various attribution methods have been developed to explain deep neural n...
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02/25/2023
Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts
In this paper, we focus on mean-field variational Bayesian Neural Networ...
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02/25/2023
Does a Neural Network Really Encode Symbolic Concept?
Recently, a series of studies have tried to extract interactions between...
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10/17/2022
Defects of Convolutional Decoder Networks in Frequency Representation
In this paper, we prove representation bottlenecks of a cascaded convolu...
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08/18/2022
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification
Compared to traditional learning from scratch, knowledge distillation so...
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07/24/2022
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
Although many methods have been proposed to enhance the transferability ...
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05/30/2022
Batch Normalization Is Blind to the First and Second Derivatives of the Loss
In this paper, we prove the effects of the BN operation on the back-prop...
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05/14/2022
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
Relational structures such as schema linking and schema encoding have be...
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05/04/2022
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
This paper aims to theoretically analyze the complexity of feature trans...
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12/02/2021
Trap of Feature Diversity in the Learning of MLPs
In this paper, we discover a two-phase phenomenon in the learning of mul...
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11/11/2021
Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models
This paper proposes a hierarchical and symbolic And-Or graph (AOG) to ob...
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11/05/2021
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
In this paper, we evaluate the quality of knowledge representations enco...
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11/05/2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial atta...
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11/05/2021
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
This paper proposes a method to visualize the discrimination power of in...
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09/22/2021
Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans
Advanced volumetric imaging methods and genetically encoded activity ind...
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07/31/2021
A Hypothesis for the Aesthetic Appreciation in Neural Networks
This paper proposes a hypothesis for the aesthetic appreciation that aes...
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07/09/2021
Interpretable Compositional Convolutional Neural Networks
The reasonable definition of semantic interpretability presents the core...
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06/21/2021
A Game-Theoretic Taxonomy of Visual Concepts in DNNs
In this paper, we rethink how a DNN encodes visual concepts of different...
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05/22/2021
Learning Baseline Values for Shapley Values
This paper aims to formulate the problem of estimating the optimal basel...
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03/12/2021
Game-theoretic Understanding of Adversarially Learned Features
This paper aims to understand adversarial attacks and defense from a new...
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10/08/2020
A Unified Approach to Interpreting and Boosting Adversarial Transferability
In this paper, we use the interaction inside adversarial perturbations t...
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09/11/2020
Achieving Adversarial Robustness via Sparsity
Network pruning has been known to produce compact models without much ac...
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06/29/2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
This paper aims to define, quantify, and analyze the feature complexity ...
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03/07/2020
Explaining Knowledge Distillation by Quantifying the Knowledge
This paper presents a method to interpret the success of knowledge disti...
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12/18/2019
Preventing Information Leakage with Neural Architecture Search
Powered by machine learning services in the cloud, numerous learning-dri...
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11/20/2019
Utility Analysis of Network Architectures for 3D Point Cloud Processing
In this paper, we diagnose deep neural networks for 3D point cloud proce...
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11/20/2019
3D-Rotation-Equivariant Quaternion Neural Networks
This paper proposes a set of rules to revise various neural networks for...
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08/05/2019
Knowledge Isomorphism between Neural Networks
This paper aims to analyze knowledge isomorphism between pre-trained dee...
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06/10/2019
Quantifying Layerwise Information Discarding of Neural Networks
This paper presents a method to explain how input information is discard...
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01/28/2019
Complex-Valued Neural Networks for Privacy Protection
This paper proposes a generic method to revise traditional neural networ...
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01/25/2019
Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
This is the Proceedings of AAAI 2019 Workshop on Network Interpretabilit...
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01/21/2019
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)
This paper presents an unsupervised method to learn a neural network, na...
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01/21/2019
Network Transplanting (extended abstract)
This paper focuses on a new task, i.e., transplanting a category-and-tas...
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01/08/2019
Interpretable CNNs
This paper proposes a generic method to learn interpretable convolutiona...
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01/08/2019
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
In this paper, we propose to disentangle and interpret contextual effect...
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12/18/2018
Explanatory Graphs for CNNs
This paper introduces a graphical model, namely an explanatory graph, wh...
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12/18/2018
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
In this paper, we present a method to mine object-part patterns from con...
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12/18/2018
Explaining Neural Networks Semantically and Quantitatively
This paper presents a method to explain the knowledge encoded in a convo...
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05/18/2018
Unsupervised Learning of Neural Networks to Explain Neural Networks
This paper presents an unsupervised method to learn a neural network, na...
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04/26/2018
Network Transplanting
This paper focuses on a novel problem, i.e., transplanting a category-an...
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02/02/2018
Visual Interpretability for Deep Learning: a Survey
This paper reviews recent studies in emerging directions of understandin...
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02/01/2018
Interpreting CNNs via Decision Trees
This paper presents a method to learn a decision tree to quantitatively ...
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10/29/2017
Examining CNN Representations with respect to Dataset Bias
Given a pre-trained CNN without any testing samples, this paper proposes...
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10/02/2017
Interpretable Convolutional Neural Networks
This paper proposes a method to modify traditional convolutional neural ...
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08/13/2017
Visual Graph Mining
In this study, we formulate the concept of "mining maximal-size frequent...
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08/13/2017