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Found 12 papers, 7 papers with code
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
Unsupervised Pretraining for Sequence to Sequence Learning
We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models.
Fast Wavenet Generation Algorithm
This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet.
Stein Variational Policy Gradient
Policy gradient methods have been successfully applied to many complex reinforcement learning problems.
Fast Generation for Convolutional Autoregressive Models
In this work, we describe a method to speed up generation in convolutional autoregressive models.
Searching for Activation Functions
The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.
Diversity and Depth in Per-Example Routing Models
Both architectural diversity and routing depth can increase the representational power of a routing network.
Ranked #2 on
Multi-Task Learning
on OMNIGLOT
Stand-Alone Self-Attention in Vision Models
The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions.
Revisiting Spatial Invariance with Low-Rank Local Connectivity
Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias.
Revisiting Fundamentals of Experience Replay
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50.
Ranked #212 on
Image Classification
on ImageNet
