signADAM and signADAM++ are new first-order optimizers that incorporate sign operations and a confidence-based sparsity mechanism, with claimed empirical superiority and theoretical convergence over ADAM and sign-based baselines.
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MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.
citing papers explorer
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signADAM: Learning Confidences for Deep Neural Networks
signADAM and signADAM++ are new first-order optimizers that incorporate sign operations and a confidence-based sparsity mechanism, with claimed empirical superiority and theoretical convergence over ADAM and sign-based baselines.
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Click-Through Rate Prediction with the User Memory Network
MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.