8,050 research outputs found
TransNets: Learning to Transform for Recommendation
Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Designing convolutional neural networks (CNN) for mobile devices is
challenging because mobile models need to be small and fast, yet still
accurate. Although significant efforts have been dedicated to design and
improve mobile CNNs on all dimensions, it is very difficult to manually balance
these trade-offs when there are so many architectural possibilities to
consider. In this paper, we propose an automated mobile neural architecture
search (MNAS) approach, which explicitly incorporate model latency into the
main objective so that the search can identify a model that achieves a good
trade-off between accuracy and latency. Unlike previous work, where latency is
considered via another, often inaccurate proxy (e.g., FLOPS), our approach
directly measures real-world inference latency by executing the model on mobile
phones. To further strike the right balance between flexibility and search
space size, we propose a novel factorized hierarchical search space that
encourages layer diversity throughout the network. Experimental results show
that our approach consistently outperforms state-of-the-art mobile CNN models
across multiple vision tasks. On the ImageNet classification task, our MnasNet
achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x
faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3x faster than
NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP
quality than MobileNets for COCO object detection. Code is at
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnetComment: Published in CVPR 201
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