Search Results for author:
Found 6 papers, 2 papers with code
Adversarial Eigen Attack on Black-Box Models
In this paper, we propose a novel setting of transferable black-box attack: attackers may use external information from a pre-trained model with available network parameters, however, different from previous studies, no additional training data is permitted to further change or tune the pre-trained model.
Learning to Select Base Classes for Few-shot Classification
Few-shot learning has attracted intensive research attention in recent years.
Learning to Learn Image Classifiers with Visual Analogy
We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class.
Font Size: Community Preserving Network Embedding
While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e. g. an threshold for outbreak).
Social and Information Networks Physics and Society
Binary Stereo Matching
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.
