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The large learning rate phase of deep learning


Authors:  Anonymous....
Published date-01/01/2021

Abstract: The choice of initial learning rate can have a profound effect on the performance of deep networks. We present empirical evidence that networks exhibit sharply distinct behaviors at small and …

Hierarchical Meta Reinforcement Learning for Multi-Task Environments


Authors:  Anonymous....
Published date-01/01/2021
Tasks:  HierarchicalReinforcementLearning, MetaReinforcementLearning

Abstract: Deep reinforcement learning algorithms aim to achieve human-level intelligence by solving practical decisions-making problems, which are often composed of multiple sub-tasks. Complex and subtle relationships between sub-tasks make traditional methods …

WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION


Authors:  Anonymous....
Published date-01/01/2021
Tasks:  Quantization

Abstract: Deep quantization of neural networks below eight bits can lead to superlinear benefits in storage and compute efficiency. However, homogeneously quantizing all the layers to the same level does not …

Colorization Transformer


Authors:  Anonymous....
Published date-01/01/2021
Tasks:  Colorization

Abstract: We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use …

Contrast to Divide: self-supervised pre-training for learning with noisy labels


Authors:  Anonymous....
Published date-01/01/2021
Tasks:  ImageClassification, Learningwithnoisylabels

Abstract: Advances in semi-supervised methods for image classification significantly boosted performance in the learning with noisy labels (LNL) task. Specifically, by discarding the erroneous labels (and keeping the samples), the LNL …

NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME


Authors:  Anonymous....
Published date-01/01/2021
Tasks:  NodeClassification

Abstract: While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt effective mechanisms to propagate the nodes' information with respect to …

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