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1,893 papers
Adaptive Self-Design Algorithm is a novel algorithmic framework designed to automate the design and optimization of mathematical formulas, particularly within complex non-standard and nonlinear equation systems. This approach leverages a probabilistic search algorithm, incorporating reinforcement learning and genetic a...
This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Po...
This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Po...
A two-stage recommendation architecture that integrates topic attention mechanisms and user interaction relationships is proposed that can maintain stable recommendation performance under different levels of interaction sparsity.
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3D-GEGCL is a framework that integrates 3D geometrically equivariant graph neural networks with self-supervised contrastive learning for robust and accurate PLBA prediction and achieves superior performance over existing leading methods across a range of assessment criteria.
Methodologically, it advances BIM-enabled resilience intelligence by integrating causal reasoning, multiplex graph learning, Bayesian uncertainty quantification and XAI within a unified framework for proactive disaster-resilient infrastructure governance and decision support.
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify model ownership and prevent significant economic losses, two groups of GNN Ownership Verification (OV) methods have been proposed: watermar...
Combining spatial clustering with adaptive attention significantly improves forecasting, enabling real-time, fine-grained monitoring and supporting personalized exposure tracking and timely alerts for healthier cities.
This paper proposes a novel framework for constructing and evolving a neuro-symbolic knowledge graph (NSKG) capable of adapting to dynamic changes in information. The core idea is to combine the strengths of deep learning and knowledge graphs, leveraging graph neural networks (GNNs) to learn relationships within the gr...
Reinforcement Learning (RL) has demonstrated remarkable success in various domains, but its application is significantly hindered by the problem of sparse rewards. In environments where rewards are infrequent or delayed, traditional RL algorithms struggle to learn effective policies. This paper proposes a novel approac...
Graph embeddings have emerged as a powerful technique for representing complex networks in a low-dimensional vector space. However, traditional graph embedding methods often lack interpretability, making it difficult to understand the underlying reasons for the generated embeddings. This paper proposes a novel dynamic...
This paper introduces a novel approach to enhancing Graph Neural Networks (GNNs) by incorporating topological information. The core idea is to explicitly model and learn the relationships between nodes and edges within a graph, leading to improved representation learning and training efficiency, particularly in complex...
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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