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Found 9 papers, 5 papers with code
Learning with Logical Constraints but without Shortcut Satisfaction
In this paper, we present a new framework for learning with logical constraints.
Softened Symbol Grounding for Neuro-symbolic Systems
Neuro-symbolic learning generally consists of two separated worlds, i. e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI.
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks
Experimental results on QNNs with different quantization bits confirm the effectiveness and efficiency of our approach, e. g., two orders of magnitude faster and able to solve more verification tasks in the same time limit than the state-of-the-art methods.
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks
Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications.
Learning Safe Neural Network Controllers with Barrier Certificates
We provide a novel approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties.
Finger Texture Biometric Characteristic: a Survey
\begin{abstract} In recent years, the Finger Texture (FT) has attracted considerable attention as a biometric characteristic.
Boosting API Recommendation with Implicit Feedback
In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance.
Making Agents' Abilities Explicit
Alternating-time temporal logics (ATL/ATL*) represent a family of modal logics for reasoning about agents' strategic abilities in multiagent systems (MAS).
