A Neural Network is a computational model designed to recognize patterns, make predictions, and perform tasks that involve learning from data. A neural network is inspired by the way biological neural networks in the human brain work . It is a key component of artificial intelligence (AI) and machine learning (ML) systems, designed to recognize patterns, make predictions, and perform tasks that involve learning from data.

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NeurIPS 2025 in San Diego from November 30th to December 5th, 2025

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NEC Laboratories America is heading to San Diego for NeurIPS 2025, where our researchers will present cutting-edge work spanning optimization, AI systems, language modeling, and trustworthy machine learning. This year’s lineup highlights breakthroughs in areas like multi-agent coordination, scalable training, efficient inference, and techniques for detecting LLM-generated text. Together, these contributions reflect our commitment to advancing fundamental science while building real-world solutions that strengthen industry and society. We’re excited to join the global AI community in San Diego from November 30 to December 5 to share our latest innovations.

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NEC Labs America Team Attending NeurIPS24 in Vancouver

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NEC Labs America is proud to attend NeurIPS 2024 in Vancouver, Canada from December 10-15. Zachary Izzo will present Subgroup Discovery with the Cox Model, Shaobo Han will present VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks and Jonathan Warrell will present Discrete-Continuous Variational Optimization with Local Gradients.

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TGNet: Learning to Rank Nodes in Temporal Graphs

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Node ranking in temporal networks are often impacted by heterogeneous context from node content, temporal, and structural dimensions. This paper introduces TGNet , a deep-learning framework for node ranking in heterogeneous temporal graphs. TGNet utilizes a variant of Recurrent Neural Network to adapt context evolution and extract context features for nodes. It incorporates a novel influence network to dynamically estimate temporal and structural influence among nodes over time. To cope with label sparsity, it integrates graph smoothness constraints as a weak form of supervision. We show that the application of TGNet is feasible for large-scale networks by developing efficient learning and inference algorithms with optimization techniques. Using real-life data, we experimentally verify the effectiveness and efficiency of TGNet techniques. We also show that TGNet yields intuitive explanations for applications such as alert detection and academic impact ranking, as verified by our case study.

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