Dilin Wang

发表

Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds

John W. Fisher, Dilin Wang, Qiang Liu, 2016, UAI.

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent pdf

Qiang Liu, Meng Li, Dilin Wang, 2019, ArXiv.

Fast Algorithm for Approximate k-Nearest Neighbor Graph Construction

Lei Shi, Dilin Wang, Jianwen Cao, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Structured Stein Variational Inference for Continuous Graphical Models pdf

Dilin Wang, Qiang Liu, Zhe Zeng, 2017, ArXiv.

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE pdf

Dilin Wang, Qiang Liu, Qiang Liu, 2017, ArXiv.

Stein Variational Gradient Descent With Matrix-Valued Kernels

Qiang Liu, Dilin Wang, Ziyang Tang, 2019, NeurIPS.

Splitting Steepest Descent for Growing Neural Architectures

Qiang Liu, Dilin Wang, Lemeng Wu, 2019, NeurIPS.

Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning

Qiang Liu, David Z. Pan, Dilin Wang, 2019, 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence pdf

Qiang Liu, Dilin Wang, Chengyue Gong, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

KeepAugment: A Simple Information-Preserving Data Augmentation Approach pdf

Qiang Liu, Dilin Wang, Meng Li, 2020, Computer Vision and Pattern Recognition.

Stein Variational Gradient Descent as Moment Matching

Qiang Liu, Dilin Wang, Qiang Liu, 2018, NeurIPS.

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Dilin Wang, Qiang Liu, Qiang Liu, 2016, NIPS.

Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models

Dilin Wang, Qiang Liu, Qiang Liu, 2019, ICML.

An Optimization View on Dynamic Routing Between Capsules

Qiang Liu, Dilin Wang, Qiang Liu, 2018, ICLR.

Improving Neural Language Modeling via Adversarial Training pdf

Dilin Wang, Qiang Liu, ChengYue Gong, 2019, ICML.

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning pdf

Dilin Wang, Qiang Liu, Qiang Liu, 2016, ArXiv.

Stein Variational Message Passing for Continuous Graphical Models

Dilin Wang, Qiang Liu, Zhe Zeng, 2017, ICML.

Entity Disambiguation by Knowledge and Text Jointly Embedding

Ming Li, Jianwen Zhang, Zheng Chen, 2016, CoNLL.

Learning to Draw Samples with Amortized Stein Variational Gradient Descent

Dilin Wang, Qiang Liu, Yihao Feng, 2017, UAI.

Parallel Construction of Approximate kNN Graph

Dilin Wang, Jianwen Cao, Yanmei Zheng, 2012, 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science.

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling pdf

Dilin Wang, Meng Li, Vikas Chandra, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Variational Inference with Tail-adaptive f-Divergence

Hao Liu, Qiang Liu, Dilin Wang, 2018, NeurIPS.

Noisy Training Improves E2E ASR for the Edge pdf

Dilin Wang, Vikas Chandra, Ozlem Kalinli, 2021, ArXiv.

Improve Vision Transformers Training by Suppressing Over-smoothing pdf

Dilin Wang, Qiang Liu, Vikas Chandra, 2021, ArXiv.

Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation pdf

David Z. Pan, Dilin Wang, Hyoukjun Kwon, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Temporally Consistent Online Depth Estimation in Dynamic Scenes pdf

Russell H. Taylor, Mathias Unberath, Dilin Wang, 2021, ArXiv.

AlphaNet: Improved Training of Supernet with Alpha-Divergence

Dilin Wang, Qiang Liu, Vikas Chandra, 2021, ICML.

Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet pdf

Dilin Wang, Vikas Chandra, Ozlem Kalinli, 2021, ArXiv.

Vision Transformers with Patch Diversification

Dilin Wang, Qiang Liu, Vikas Chandra, 2021, 2104.12753.

Streaming Transformer Transducer based Speech Recognition Using Non-Causal Convolution pdf

Dilin Wang, Ozlem Kalinli, Jay Mahadeokar, 2021, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).