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  • HOW CLUSTERING APPROACHES AFFECT THE OPTIMAL DESIGN OF FUTURE MULTI-ENERGY SYSTEMS

    ECOS 2024

    HOW CLUSTERING APPROACHES AFFECT THE OPTIMAL DESIGN OF FUTURE MULTI-ENERGY SYSTEMS

    Alessandro Pampado, Gabriele Volpato, Andrea Lazzaretto p1481-1492 from 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)
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  • How Control Information Influences Multilingual Text Image Generation and Editing?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Control Information Influences Multilingual Text Image Generation and Editing?

    Boqiang Zhang, Zuan Gao, Yadong Qu, Hongtao Xie p6884-6904 from Advances in Neural Information Processing Systems 37
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  • How Diffusion Models Learn to Factorize and Compose

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Diffusion Models Learn to Factorize and Compose

    Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete p15121-15148 from Advances in Neural Information Processing Systems 37
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  • How Do Large Language Models Acquire Factual Knowledge During Pretraining?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Do Large Language Models Acquire Factual Knowledge During Pretraining?

    Hoyeon Chang, Jinho Park, Seonghyeon Ye, Sohee Yang, Youngkyung Seo, Du-Seong Chang, Minjoon Seo p60626-60668 from Advances in Neural Information Processing Systems 37
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  • How do Large Language Models Handle Multilingualism?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How do Large Language Models Handle Multilingualism?

    Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, Lidong Bing p15296-15319 from Advances in Neural Information Processing Systems 37
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  • How do Minimum-Norm Shallow Denoisers Look in Function Space?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How do Minimum-Norm Shallow Denoisers Look in Function Space?

    Chen Zeno, Greg Ongie, Yaniv Blumenfeld, Nir Weinberger, Daniel Soudry p57520-57557 from Advances in Neural Information Processing Systems 36
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  • How do Teachers Respond to Sustained Change?

    Research In Engineering Education Network

    How do Teachers Respond to Sustained Change?

    Rodger Hadgraft, Franziska Trede, Monika Rummler p1132-1133 from 9th Research in Engineering Education Symposium and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021)
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  • How Does Adaptive Optimization Impact Local Neural Network Geometry?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Does Adaptive Optimization Impact Local Neural Network Geometry?

    Kaiqi Jiang, Dhruv Malik, Yuanzhi Li p8305-8384 from Advances in Neural Information Processing Systems 36
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  • How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers

    Xin Lu, Yanyan Zhao, Bing Qin, Liangyu Huo, Qing Yang, Dongliang Xu p87411-87437 from Advances in Neural Information Processing Systems 37
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  • How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?

    Jun Chen, Hong Chen, Bin Gu p107745-107794 from Advances in Neural Information Processing Systems 37
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  • How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model

    Michael Hanna, Ollie Liu, Alexandre Variengien p76033-76060 from Advances in Neural Information Processing Systems 36
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  • How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks

    Mo Zhou, Rong Ge p46481-46538 from Advances in Neural Information Processing Systems 37
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