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Found 11 papers, 4 papers with code
UMEC: Unified model and embedding compression for efficient recommendation systems
The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios.
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
The trained model could be adjusted among different standard and robust accuracies "for free" at testing time.
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices.
Hierarchical Prototype Learning for Zero-Shot Recognition
Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting.
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
In this paper, we take an initial attempt, and propose a generic formulation to provide a systematical solution (named ATZSL) for learning a robust ZSL model.
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
The key problem in graph node embedding lies in how to define the dependence to neighbors.
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.
P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.
On The Projection Operator to A Three-view Cardinality Constrained Set
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing.
