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Found 16 papers, 3 papers with code
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-trained AI models.
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 (LLMs) as a set of symbolic concepts.
Concept-Level Explanation for the Generalization of a DNN
Therefore, in this paper, we investigate the generalization power of each interactive concept, and we use the generalization power of different interactive concepts to explain the generalization power of the entire DNN.
Defects of Convolutional Decoder Networks in Frequency Representation
In this paper, we prove the representation defects of a cascaded convolutional decoder network, considering the capacity of representing different frequency components of an input sample.
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-propagation of the first and second derivatives of the loss.
Why Adversarial Training of ReLU Networks Is Difficult?
We also prove that adversarial training tends to strengthen the influence of unconfident input samples with large gradient norms in an exponential manner.
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing.
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach
We propose a model-based multi-agent reinforcement learning attack framework against federated learning systems.
Interpretable Compositional Convolutional Neural Networks
The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network
Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM).
3D-Rotation-Equivariant Quaternion Neural Networks
This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs).
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures.
Information Design in Crowdfunding under Thresholding Policies
We introduce a heuristic algorithm to dynamically compute information-disclosure policies for the entrepreneur, followed by an empirical evaluation to demonstrate its competitiveness over the widely-adopted immediate-disclosure policy.
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy.
An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems
With proper management, Autonomous Mobility-on-Demand (AMoD) systems have great potential to satisfy the transport demands of urban populations by providing safe, convenient, and affordable ridesharing services.
Managing Autonomous Mobility on Demand Systems for Better Passenger Experience
Autonomous mobility on demand systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future.
