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13 papers · 1 filter
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
Longyuan Li, Jihai Zhang, Junchi Yan +4
Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynch…
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…
New Interpretations of Normalization Methods in Deep Learning
Jiacheng Sun, Xiangyong Cao, Hanwen Liang +3
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalizati…
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Cong Fei, Bin Wang, Yuzheng Zhuang +5
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However,…