Action Anticipation with RBF Kernelized Feature Mapping RNN von Shi, Yuge, Fernando, Basura, Hartley, Richard

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Memory Consolidation Enables Long-Context Video Understanding von Balažević, Ivana, Shi, Yuge, Papalampidi, Pinelopi, Chaabouni, Rahma, Koppula, Skanda, Hénaff, Olivier J

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Open-Endedness is Essential for Artificial Superhuman Intelligence von Hughes, Edward, Dennis, Michael, Parker-Holder, Jack, Behbahani, Feryal, Mavalankar, Aditi, Shi, Yuge, Schaul, Tom, Rocktaschel, Tim

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Action Anticipation with RBF Kernelized Feature Mapping RNN von Shi, Yuge, Fernando, Basura, Hartley, Richard

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Tuning computer vision models with task rewards von Pinto, André Susano, Kolesnikov, Alexander, Shi, Yuge, Beyer, Lucas, Zhai, Xiaohua

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How Robust is Unsupervised Representation Learning to Distribution Shift? von Shi, Yuge, Daunhawer, Imant, Vogt, Julia E, Torr, Philip H. S, Sanyal, Amartya

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Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models von Shi, Yuge, Paige, Brooks, Torr, Philip H. S, Siddharth, N

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Adversarial Masking for Self-Supervised Learning von Shi, Yuge, Siddharth, N, Torr, Philip H. S, Kosiorek, Adam R

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Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models von Shi, Yuge, Siddharth, N, Paige, Brooks, Torr, Philip H. S

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Learning Multimodal VAEs through Mutual Supervision von Joy, Tom, Shi, Yuge, Torr, Philip H. S, Rainforth, Tom, Schmon, Sebastian M, Siddharth, N

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Gradient Matching for Domain Generalization von Shi, Yuge, Seely, Jeffrey, Torr, Philip H. S, Siddharth, N, Hannun, Awni, Usunier, Nicolas, Synnaeve, Gabriel

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Action Anticipation with RBF Kernelized Feature Mapping RNN von Yuge Shi, Basura Fernando, Hartley, Richard

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FINE-TUNING COMPUTER VISION NEURAL NEWORKS USING TASK REWARDS von Beyer, Lucas Klaus, Zhai, Xiaohua, Kolesnikov, Alexander, Shi, Yuge, Susano Pinto, André

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FINE-TUNING COMPUTER VISION NEURAL NEWORKS USING TASK REWARDS von BEYER, Lucas Klaus, KOLESNIKOV, Alexander, SUSANO PINTO, André, ZHAI, Xiaohua, SHI, Yuge

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Genie: Generative Interactive Environments von Bruce, Jake, Dennis, Michael, Edwards, Ashley, Parker-Holder, Jack, Shi, Yuge, Hughes, Edward, Lai, Matthew, Mavalankar, Aditi, Steigerwald, Richie, Apps, Chris, Aytar, Yusuf, Bechtle, Sarah, Behbahani, Feryal, Chan, Stephanie, Heess, Nicolas, Gonzalez, Lucy, Osindero, Simon, Ozair, Sherjil, Reed, Scott, Zhang, Jingwei, Zolna, Konrad, Clune, Jeff, de Freitas, Nando, Singh, Satinder, Rocktäschel, Tim

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Memory Consolidation Enables Long-Context Video Understanding von Balažević, Ivana, Yuge Shi, Papalampidi, Pinelopi, Chaabouni, Rahma, Koppula, Skanda, Hénaff, Olivier J

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振荡提取-荧光分光光度法分析土壤样品中石油类物质 von 李媛, 段小燕, 施玉格, 李刚

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Tuning computer vision models with task rewards von André Susano Pinto, Kolesnikov, Alexander, Yuge Shi, Beyer, Lucas, Zhai, Xiaohua

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Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models von Yuge Shi, Brooks, Paige, Torr, Philip H S, Siddharth, N

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How Robust is Unsupervised Representation Learning to Distribution Shift? von Yuge Shi, Daunhawer, Imant, Vogt, Julia E, Torr, Philip H S, Sanyal, Amartya

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