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FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
YongganFu, HaoranYou, YangZhao....
Published date-12/01/2020
Quantization
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due …
Sim2Real for Self-Supervised Monocular Depth and Segmentation
NithinRaghavan, PunarjayChakravarty, ShubhamShrivastava....
Published date-12/01/2020
DomainAdaptation
Image-based learning methods for autonomous vehicle perception tasks require large quantities of labelled, real data in order to properly train without overfitting, which can often be incredibly costly. While leveraging …
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
JiaxingWang, HaoliBai, JiaxiangWu....
Published date-12/01/2020
NeuralArchitectureSearch
Recent advances in neural architecture search inspire many channel number search algorithms~(CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different …
Fully Convolutional Networks for Panoptic Segmentation
YanweiLi, HengshuangZhao, XiaojuanQi....
Published date-12/01/2020
PanopticSegmentation
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff …
Evaluating Attribution for Graph Neural Networks
BenjaminSanchez-Lengeling, JenniferWei, BrianLee....
Published date-12/01/2020
Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging. Attribution is one approach to interpretability, which highlights input dimensions that are influential to …
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms
AaronJ.Snoswell, SuryaP.N.Singh, NanYe....
Published date-12/01/2020
OpenAIGym
We provide new perspectives and inference algorithms for Maximum Entropy (MaxEnt) Inverse Reinforcement Learning (IRL), which provides a principled method to find a most non-committal reward function consistent with given …