NeurIPS
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A quick guide to Amazon’s papers at NeurIPS 2022
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The Conference on Neural Information Processing Systems (NeurIPS) remains the highest-profile conference in AI, and as such, it draws paper submissions from ...

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NeurIPS: Why causal-representation learning may be the future of AI
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In a conversation right before the 2021 Conference on Neural Information Processing Systems (NeurIPS), Amazon vice president and distinguished scientist ...

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How a NeurIPS workshop is increasing women’s visibility in AI
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A. I absolutely think so. This is one of the most respected workshops at NeurIPS, because it has been going for a long time and the quality is pretty high. ...

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Preventing updated NLP models from backsliding on particular tasks
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Machine learning (ML) models need regular updates to improve performance, but retraining a model poses risks, such as the loss of backward compatibility or ...

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In reinforcement learning, slower networks can learn faster
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Reinforcement learning (RL) is an increasingly popular way to model sequential decision-making problems in artificial intelligence. RL agents learn through ...

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Personalized federated learning for a better customer experience
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Federated learning (FL) is a framework that allows edge devices (e.g., Alexa devices) to collaboratively train a global model while keeping customers’ data ...

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Amazon releases code, datasets for developing embodied AI agents
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Alexa Arena is a new embodied-AI framework developed to push the boundaries of human-robot interaction. It offers an interactive, user-centric framework for ...

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Making deep learning practical for Earth system forecasting
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The Earth is a complex system. Variabilities ranging from regular events like temperature fluctuations to extreme events like drought, hailstorms, and the ...

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At NeurIPS, what’s old is new again
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The current excitement around large language models is just the latest aftershock of the deep-learning revolution that started in 2012 (or maybe 2010), but ...

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Real-time anomaly detection under distribution drift
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Anomaly detection seeks to identify behaviors that lie outside statistical norms. Anomalies could indicate some kind of malicious activity, such as attempts ...

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