20,123 research outputs found
Learning and Transferring IDs Representation in E-commerce
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Intermediate features at different layers of a deep neural network are known
to be discriminative for visual patterns of different complexities. However,
most existing works ignore such cross-layer heterogeneities when classifying
samples of different complexities. For example, if a training sample has
already been correctly classified at a specific layer with high confidence, we
argue that it is unnecessary to enforce rest layers to classify this sample
correctly and a better strategy is to encourage those layers to focus on other
samples.
In this paper, we propose a layer-wise discriminative learning method to
enhance the discriminative capability of a deep network by allowing its layers
to work collaboratively for classification. Towards this target, we introduce
multiple classifiers on top of multiple layers. Each classifier not only tries
to correctly classify the features from its input layer, but also coordinates
with other classifiers to jointly maximize the final classification
performance. Guided by the other companion classifiers, each classifier learns
to concentrate on certain training examples and boosts the overall performance.
Allowing for end-to-end training, our method can be conveniently embedded into
state-of-the-art deep networks. Experiments with multiple popular deep
networks, including Network in Network, GoogLeNet and VGGNet, on scale-various
object classification benchmarks, including CIFAR100, MNIST and ImageNet, and
scene classification benchmarks, including MIT67, SUN397 and Places205,
demonstrate the effectiveness of our method. In addition, we also analyze the
relationship between the proposed method and classical conditional random
fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before
camera-ready versio
A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation
The objective of deep learning methods based on encoder-decoder architectures
for music source separation is to approximate either ideal time-frequency masks
or spectral representations of the target music source(s). The spectral
representations are then used to derive time-frequency masks. In this work we
introduce a method to directly learn time-frequency masks from an observed
mixture magnitude spectrum. We employ recurrent neural networks and train them
using prior knowledge only for the magnitude spectrum of the target source. To
assess the performance of the proposed method, we focus on the task of singing
voice separation. The results from an objective evaluation show that our
proposed method provides comparable results to deep learning based methods
which operate over complicated signal representations. Compared to previous
methods that approximate time-frequency masks, our method has increased
performance of signal to distortion ratio by an average of 3.8 dB
- …