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Found 10 papers, 2 papers with code
Test-Time Model Adaptation with Only Forward Passes
However, in real-world scenarios, models are usually deployed on resource-limited devices, e. g., FPGAs, and are often quantized and hard-coded with non-modifiable parameters for acceleration.
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
Multi-scale Attention Flow for Probabilistic Time Series Forecasting
The probability prediction of multivariate time series is a notoriously challenging but practical task.
A Survey on Reinforcement Learning for Recommender Systems
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems.
Adaptive Course Recommendation System
In this paper, we propose a novel course recommendation framework, named Dynamic Attention and hierarchical Reinforcement Learning (DARL), to improve the adaptivity of the recommendation model.
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory.
Face Detection using Deep Learning: An Improved Faster RCNN Approach
In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation.
A Framework of Sparse Online Learning and Its Applications
Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification.
