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PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
TLDR
This paper presents a novel approach, namely Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. Expand
Heterogeneous Network Embedding via Deep Architectures
TLDR
It is demonstrated that the rich content and linkage information in a heterogeneous network can be captured by a multi-resolution deep embedding function, so that similarities among cross-modal data can be measured directly in a common embedding space. Expand
PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search
TLDR
This paper presents a novel approach, namely, Partially-Connected DARTS, by sampling a small part of super-network to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. Expand
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
  • Guo-Jun Qi
  • Computer Science
  • International Journal of Computer Vision
  • 23 January 2017
TLDR
The Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN) are presented, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN. Expand
Differential Recurrent Neural Networks for Action Recognition
TLDR
This study proposes a differential gating scheme for the LSTM neural network, which emphasizes on the change in information gain caused by the salient motions between the successive frames, and thus the model is termed as differential Recurrent Neural Network (dRNN). Expand
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
TLDR
A novel State Frequency Memory (SFM) recurrent network is proposed to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Expand
Unified Video Annotation via Multigraph Learning
TLDR
This paper shows that various crucial factors in video annotation, including multiple modalities, multiple distance functions, and temporal consistency, all correspond to different relationships among video units, and hence they can be represented by different graphs, and proposes optimized multigraph-based semi-supervised learning (OMG-SSL), which aims to simultaneously tackle these difficulties in a unified scheme. Expand
Correlative multi-label video annotation
TLDR
A third paradigm is proposed which simultaneously classifies concepts and models correlations between them in a single step by using a novel Correlative Multi-Label (CML) framework and is compared with the state-of-the-art approaches in the first and second paradigms on the widely used TRECVID data set. Expand
Joint multi-label multi-instance learning for image classification
TLDR
This work proposes an integrated multi- label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation. Expand
Task Agnostic Meta-Learning for Few-Shot Learning
TLDR
An entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks, which outperforms compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks. Expand
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