Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov
Neural Information Processing Systems
Neural Information Processing Systems http://nips.cc/
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Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Neural Information Processing Systems
Accurately modeling the correlation structure of errors is critical for reliable uncertainty quantification in probabilistic time series forecasting.
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On the Downstream Performance of Compressed Word Embeddings
Avner May, Jian Zhang, Tri Dao, Christopher Rรฉ
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Neural Information Processing Systems http://nips.cc/
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Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Neural Information Processing Systems
However, for many applications this formulation is insufficient.
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Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, Gregory Wayne
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Neural Information Processing Systems http://nips.cc/
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