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Found 8 papers, 5 papers with code
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising and recommender systems.
Continual Local Training for Better Initialization of Federated Models
Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy concerns.
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required.
Cross-Scale Cost Aggregation for Stereo Matching
We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels.
Binary Stereo Matching
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.
