PLeaS -- Merging Models with Permutations and Least Squares von Nasery, Anshul, Hayase, Jonathan, Koh, Pang Wei, Oh, Sewoong

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PEEKABOO: Interactive Video Generation via Masked-Diffusion von Jain, Yash, Nasery, Anshul, Vineet, Vibhav, Behl, Harkirat

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Label Differential Privacy via Aggregation von Brahmbhatt, Anand, Saket, Rishi, Havaldar, Shreyas, Nasery, Anshul, Raghuveer, Aravindan

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End-to-End Neural Network Compression via $\frac{\ell_1}{\ell_2}$ Regularized Latency Surrogates von Nasery, Anshul, Shah, Hardik, Suggala, Arun Sai, Jain, Prateek

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DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization von Nasery, Anshul, Addepalli, Sravanti, Netrapalli, Praneeth, Jain, Prateek

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Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias von Mohla, Satyam, Nasery, Anshul, Banerjee, Biplab

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Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time von Nasery, Anshul, Thakur, Soumyadeep, Piratla, Vihari, De, Abir, Sarawagi, Sunita

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Rule Augmented Unsupervised Constituency Parsing von Sahay, Atul, Nasery, Anshul, Maheshwari, Ayush, Ramakrishnan, Ganesh, Iyer, Rishabh

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OML: Open, Monetizable, and Loyal AI von Cheng, Zerui, Contente, Edoardo, Finch, Ben, Golev, Oleg, Hayase, Jonathan, Miller, Andrew, Moshrefi, Niusha, Nasery, Anshul, Nailwal, Sandeep, Oh, Sewoong, Tyagi, Himanshu, Viswanath, Pramod

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Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks von Addepalli, Sravanti, Nasery, Anshul, Babu, R. Venkatesh, Netrapalli, Praneeth, Jain, Prateek

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PLeaS -- Merging Models with Permutations and Least Squares von Nasery, Anshul, Hayase, Jonathan, Pang Wei Koh, Oh, Sewoong

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PEEKABOO: Interactive Video Generation via Masked-Diffusion von Jain, Yash, Nasery, Anshul, Vibhav Vineet, Behl, Harkirat

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Label Differential Privacy via Aggregation von Brahmbhatt, Anand, Saket, Rishi, Havaldar, Shreyas, Nasery, Anshul, Aravindan Raghuveer

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Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias von Mohla, Satyam, Nasery, Anshul, Banerjee, Biplab

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End-to-End Neural Network Compression via \(\frac{\ell_1}{\ell_2}\) Regularized Latency Surrogates von Nasery, Anshul, Shah, Hardik, Arun Sai Suggala, Jain, Prateek

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What if Neural Networks had SVDs? von Mathiasen, Alexander, Hvilshøj, Frederik, Jørgensen, Jakob Rødsgaard, Nasery, Anshul, Mottin, Davide

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DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization von Nasery, Anshul, Addepalli, Sravanti, Praneeth Netrapalli, Jain, Prateek

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Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time von Nasery, Anshul, Thakur, Soumyadeep, Piratla, Vihari, De, Abir, Sarawagi, Sunita

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Rule Augmented Unsupervised Constituency Parsing von Sahay, Atul, Nasery, Anshul, Maheshwari, Ayush, Ramakrishnan, Ganesh, Iyer, Rishabh

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OML: Open, Monetizable, and Loyal AI von Cheng, Zerui, Contente, Edoardo, Finch, Ben, Golev, Oleg, Hayase, Jonathan, Miller, Andrew, Moshrefi, Niusha, Nasery, Anshul, Nailwal, Sandeep, Oh, Sewoong, Tyagi, Himanshu, Viswanath, Pramod

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