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Axiomatic Attribution for Deep Networks
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Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
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Bayesian Models of Data Streams with Hierarchical Power Priors
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Attentive Recurrent Comparators
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Automated Curriculum Learning for Neural Networks
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Automatic Discovery of the Statistical Types of Variables in a Dataset
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Approximate Newton Methods and Their Local Convergence
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Asynchronous Distributed Variational Gaussian Process for Regression
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Asymmetric Tri-training for Unsupervised Domain Adaptation
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Asynchronous Stochastic Gradient Descent with Delay Compensation
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Analysis and Optimization of Graph Decompositions by Lifted Multicuts
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Approximate Steepest Coordinate Descent
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Analytical Guarantees on Numerical Precision of Deep Neural Networks
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An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation
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An Infinite Hidden Markov Model With Similarity-Biased Transitions
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Analogical Inference for Multi-relational Embeddings
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Algorithms for $ ell_p$ Low-Rank Approximation
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An Adaptive Test of Independence with Analytic Kernel Embeddings
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Algorithmic Stability and Hypothesis Complexity
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An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis
AnAnalyticalFormulaofPopulationGradientfortwo-layeredReLUnetworkanditsApplicationsinConvergenceandCriticalPointAnalysisYuandongTian1Abstractandwhysimplemethodslikegradientdescentcansolvethecomplica...
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