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Mengye Ren

Mengye Ren
Mengye Ren is a researcher/engineer at Uber Advanced Technologies Group Toronto.

Engineering Blog Articles

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.

Research Papers

Incremental Few-Shot Learning with Attention Attractor Networks

M. Ren, R. Liao, E. Fetaya, R. Zemel
This paper addresses the problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes; and several extra novel classes are being considered, each with only a few labeled examples. [...] [PDF at]
Meta Learning workshop @ NeurIPS (NeurIPS), 2018

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

Y. Wu, M. Ren, R. Liao, R. Grosse
Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is unrolled. [...] [PDF at arXiv]
International Conference on Machine Learning (ICLR), 2018

Meta-Learning for Semi-Supervised Few-Shot Classification

M. Ren, E. Triantafilou, S. Ravi, J. Snell, K. Swersky, J. Tenenbaum, H. Larochelle, R. Zemel
In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. [...] [PDF at University of Toronto]
Code & Datasets: [LINK at GitHub]
International Conference on Machine Learning (ICLR), 2018

Learning to Reweight Examples for Robust Deep Learning

M. Ren, W. Zeng, B. Yang, R. Urtasun
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern ( ICML), 2018

SBNet: Sparse Block’s Network for Fast Inference

M. Ren, A. Pokrovsky, B. Yang, R. Urtasun
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

The Reversible Residual Network: Backpropagation Without Storing Activations

A. Gomez, M. Ren, Raquel Urtasun, R. Grosse
Residual Networks (ResNets) have demonstrated significant improvement over traditional Convolutional Neural Networks (CNNs) on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck as one needs to store all the intermediate activations for calculating gradients using backpropagation. [...] [PDF at NeurIPS Proceedings]
Advances in Neural Information Processing Systems (NIPS), 2017

End-To-End Instance Segmentation With Recurrent Attention

M. Ren, R. Zemel
While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. [...] [PDF at University of Toronto]
Supplementary Materials: [LINK at University of Toronto]
Code: [LINK at GitHub]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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