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ORCIDDilin Wang
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2020 – today
- 2020
[i13]Dilin Wang, Meng Li, Chengyue Gong, Vikas Chandra:
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling. CoRR abs/2011.09011 (2020)- [i12]Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu:
KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CoRR abs/2011.11778 (2020) - [i11]Chengyue Gong, Dilin Wang, Qiang Liu:
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence. CoRR abs/2011.11779 (2020)
2010 – 2019
- 2019
- [c14]ChengYue Gong, Zixuan Jiang, Dilin Wang, Yibo Lin, Qiang Liu, David Z. Pan:
Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning. ICCAD 2019: 1-7 - [c13]Dilin Wang, ChengYue Gong, Qiang Liu:
Improving Neural Language Modeling via Adversarial Training. ICML 2019: 6555-6565 - [c12]Dilin Wang, Qiang Liu:
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. ICML 2019: 6576-6585 - [c11]Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu:
Stein Variational Gradient Descent With Matrix-Valued Kernels. NeurIPS 2019: 7834-7844 - [c10]Lemeng Wu, Dilin Wang, Qiang Liu:
Splitting Steepest Descent for Growing Neural Architectures. NeurIPS 2019: 10655-10665 - [i10]Dilin Wang, ChengYue Gong, Qiang Liu:
Improving Neural Language Modeling via Adversarial Training. CoRR abs/1906.03805 (2019) - [i9]Qiang Liu, Lemeng Wu, Dilin Wang:
Splitting Steepest Descent for Growing Neural Architectures. CoRR abs/1910.02366 (2019) - [i8]Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu:
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent. CoRR abs/1910.03103 (2019) - [i7]Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu:
Stein Variational Gradient Descent With Matrix-Valued Kernels. CoRR abs/1910.12794 (2019) - 2018
- [c9]Dilin Wang, Qiang Liu:
An Optimization View on Dynamic Routing Between Capsules. ICLR (Workshop) 2018 - [c8]Dilin Wang, Zhe Zeng, Qiang Liu:
Stein Variational Message Passing for Continuous Graphical Models. ICML 2018: 5206-5214 - [c7]Dilin Wang, Hao Liu, Qiang Liu:
Variational Inference with Tail-adaptive f-Divergence. NeurIPS 2018: 5742-5752 - [c6]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent as Moment Matching. NeurIPS 2018: 8868-8877 - [i6]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent as Moment Matching. CoRR abs/1810.11693 (2018) - [i5]Dilin Wang, Hao Liu, Qiang Liu:
Variational Inference with Tail-adaptive f-Divergence. CoRR abs/1810.11943 (2018) - 2017
- [c5]Yihao Feng, Dilin Wang, Qiang Liu:
Learning to Draw Samples with Amortized Stein Variational Gradient Descent. UAI 2017 - [i4]Qiang Liu, Dilin Wang:
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE. CoRR abs/1707.00797 (2017) - [i3]Dilin Wang, Zhe Zeng, Qiang Liu:
Structured Stein Variational Inference for Continuous Graphical Models. CoRR abs/1711.07168 (2017) - 2016
- [c4]Wei Fang, Jianwen Zhang, Dilin Wang, Zheng Chen, Ming Li:
Entity Disambiguation by Knowledge and Text Jointly Embedding. CoNLL 2016: 260-269 - [c3]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. NIPS 2016: 2370-2378 - [c2]Dilin Wang, John W. Fisher III, Qiang Liu:
Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds. UAI 2016 - [i2]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. CoRR abs/1608.04471 (2016) - [i1]Dilin Wang, Qiang Liu:
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. CoRR abs/1611.01722 (2016) - 2013
- [c1]Dilin Wang, Lei Shi, Jianwen Cao:
Fast Algorithm for Approximate k-Nearest Neighbor Graph Construction. ICDM Workshops 2013: 349-356
Coauthor Index
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