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The large learning rate phase of deep learning
Anonymous....
Published date-01/01/2021
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
Anonymous....
Published date-01/01/2021
HierarchicalReinforcementLearning, MetaReinforcementLearning
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
Anonymous....
Published date-01/01/2021
Quantization
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
Anonymous....
Published date-01/01/2021
Colorization
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
Anonymous....
Published date-01/01/2021
ImageClassification, Learningwithnoisylabels
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
Anonymous....
Published date-01/01/2021
NodeClassification
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 …