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2,295 papers
This paper proposes a novel approach to modeling human memory by representing it as a dynamic cognitive graph. The core idea is to map the complex neural activation patterns observed during memory processes onto a graph structure where nodes represent concepts and edges represent the strength and temporal relationships...
THGNN-MRD, a temporal heterogeneous graph neural network for multimodal rumor detection, is proposed, suggesting that modeling rumors as evolving multimodal social events provides a principled and effective solution for trustworthy rumor detection.
The proposed CSGSACNN-HMOA framework operates to safeguard data integrity and patient safety at the same time as facilitating secure deployment in scalable healthcare environments and reaches superior results in every essential measurement aspect.
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...
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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.
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.
LeJEPA is adapted to molecular graphs, evaluating GPS and Chemprop-style D-MPNN encoders on the antibiotic-activity dataset and ogbg-molhiv using a multi-seed, bootstrap-based protocol, and pretraining supplies complementary information best realised through feature-level combination, while finetuning gains are weak an...
A Robust watErMArk-based fingeRprint frameworK for GNNs, named REMARK, which first generates carefully crafted in-distribution watermark graphs that maximize output differences between GNN models, thus mitigating OOD-induced performance degradation.
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.
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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.
Climate & Sustainability
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