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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CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
Neural Information Processing Systems
To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.
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DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning Hengli Li
Neural Information Processing Systems
Along with the dataset, two tasks are proposed to evaluate machines'
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