Paper

  • The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better

    Scott Geng, Cheng-Yu Hsieh, Pang Koh, Ranjay Krishna, Chun-Liang Li, Vivek Ramanujan, Matthew Wallingford p7902-7929 from Advances in Neural Information Processing Systems 37
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  • The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

    Guillem Braso, Ismail Elezi, Peter Kocsis, Laura Leal-Taixé, Matthias Niessner, Peter Súkeník p1896-1908 from Advances in Neural Information Processing Systems 35
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  • The Unreliability of Explanations in Few-Shot Prompting for Textual Reasoning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Unreliability of Explanations in Few-Shot Prompting for Textual Reasoning

    Greg Durrett, Xi Ye p30378-30392 from Advances in Neural Information Processing Systems 35
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  • THE USE OF PARAMETRIC WORKFLOW ON TIMBER CONSTRUCTION AT SERVICE STATION TORGHATTEN

    World Conference on Timber Engineering 2023 (WCTE 2023)

    THE USE OF PARAMETRIC WORKFLOW ON TIMBER CONSTRUCTION AT SERVICE STATION TORGHATTEN

    Paul Hufnagel, Philipp Vehrenberg, Matthias Stracke p2570-2576 from World Conference on Timber Engineering (WCTE 2023)
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  • The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes

    Weipeng Huang, Eoin Kenny p52907-52935 from Advances in Neural Information Processing Systems 36
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  • The Value of Reward Lookahead in Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Value of Reward Lookahead in Reinforcement Learning

    Dorian Baudry, Nadav Merlis, Vianney Perchet p83627-83664 from Advances in Neural Information Processing Systems 37
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  • The Waymo Open Sim Agents Challenge

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Waymo Open Sim Agents Challenge

    Dragomir Anguelov, Tristan Emrich, Cole Gulino, Alex Kuefler, John Lambert, Michelle Li, Nico Montali, Paul Mougin, Nicholas Rhinehart, Brandyn White, Shimon Whiteson, Zoey Yang p59151-59171 from Advances in Neural Information Processing Systems 36
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  • THE WEDISTRICT TOOL: AN OPEN-ACCESS WEB APPLICATION TO SUPPORT DECISIONS ON DISTRICT HEATING AND COOLING PROJECTS AROUND EUROPE.

    ECOS 2024

    THE WEDISTRICT TOOL: AN OPEN-ACCESS WEB APPLICATION TO SUPPORT DECISIONS ON DISTRICT HEATING AND COOLING PROJECTS AROUND EUROPE.

    Alberto Abánades, Juan José Roncal Casano, Javier Rodríguez-Martín, Joaquim Romaní, Santiago Escudero, María Guadalupe Rodríguez, Nataliia Diatlova, Federico Bava, Sebastian Wulff, Jon Martínez Fontecha p1211-1222 from 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)
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  • The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

    Fruzsina Agocs, Miguel Beneitez, Marsha Berger, Bruno Blancard, Blakesley Burkhart, Miles Cranmer, Stuart Dalziel, Drummond Fielding, Daniel Fortunato, Jared Goldberg, Keiya Hirashima, Shirley Ho, Yan-Fei Jiang, Rich Kerswell, et al. p44989-45037 from Advances in Neural Information Processing Systems 37
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  • Theoretical Analysis of Deep Neural Networks for Temporally Dependent Observations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of Deep Neural Networks for Temporally Dependent Observations

    Mingliang Ma, Abolfazl Safikhani p37324-37334 from Advances in Neural Information Processing Systems 35
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  • Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks

    Zihao Wang, Lei Wu p74289-74338 from Advances in Neural Information Processing Systems 36
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  • Theoretical Analysis of Weak-to-Strong Generalization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Theoretical Analysis of Weak-to-Strong Generalization

    Hunter Lang, David Sontag, Aravindan Vijayaraghavan p46837-46880 from Advances in Neural Information Processing Systems 37
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