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Found 12 papers, 5 papers with code
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.
Sill-Net: Feature Augmentation with Separated Illumination Representation
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.
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Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples
To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback.
Learning Fast Approximations of Sparse Nonlinear Regression
The idea of unfolding iterative algorithms as deep neural networks has been widely applied in solving sparse coding problems, providing both solid theoretical analysis in convergence rate and superior empirical performance.
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.
Adversarial Margin Maximization Networks
The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability.
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficulty of estimating gradients.
Deep Defense: Training DNNs with Improved Adversarial Robustness
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems.
Zero-Shot Learning by Generating Pseudo Feature Representations
Given the dataset of seen classes and side information of unseen classes (e. g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor.
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Object detection when provided image-level labels instead of instance-level labels (i. e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain.
Neural Network Architecture Optimization through Submodularity and Supermodularity
Deep learning models' architectures, including depth and width, are key factors influencing models' performance, such as test accuracy and computation time.
Optimizing Recurrent Neural Networks Architectures under Time Constraints
A greedy algorithm with bounds is suggested to solve the transformed problem.
