841 citations · 2.6k across the 48 of their papers we have counts for
6 papers · 1 filter
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren, Renjie Liao, Ethan Fetaya +1
Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning a…
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel Urtasun
Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitivel…
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Yuhuai Wu, Mengye Ren, Renjie Liao +1
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-optimiza…
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi +5
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…
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang +1
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 t…
SBNet: Sparse Blocks Network for Fast Inference
Mengye Ren, Andrei Pokrovsky, Bin Yang +1
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computatio…