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Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in ...
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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Continual learning usually assumes the incoming data are fully labeled, ...
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MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image reco...
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DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
While deep learning demonstrates its strong ability to handle independen...
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AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Learning sophisticated feature interactions is crucial for Click-Through...
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Batch Group Normalization
Deep Convolutional Neural Networks (DCNNs) are hard and time-consuming t...
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MOFA: Modular Factorial Design for Hyperparameter Optimization
Automated hyperparameter optimization (HPO) has shown great power in man...
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Penalty and Augmented Lagrangian Methods for Layer-parallel Training of Residual Networks
Algorithms for training residual networks (ResNets) typically require fo...
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CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
We address the curve lane detection problem which poses more realistic c...
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AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling
Most state-of-the-art object detection systems follow an anchor-based di...
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An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization
The evaluation of hyperparameters, neural architectures, or data augment...
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Decoder-free Robustness Disentanglement without (Additional) Supervision
Adversarial Training (AT) is proposed to alleviate the adversarial vulne...
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New Interpretations of Normalization Methods in Deep Learning
In recent years, a variety of normalization methods have been proposed t...
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Risk Variance Penalization: From Distributional Robustness to Causality
Learning under multi-environments often requires the ability of out-of-d...
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Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this p...
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Boosting Few-Shot Learning With Adaptive Margin Loss
Few-shot learning (FSL) has attracted increasing attention in recent yea...
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Rethinking Performance Estimation in Neural Architecture Search
Neural architecture search (NAS) remains a challenging problem, which is...
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AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Learning effective feature interactions is crucial for click-through rat...
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EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
Object detectors trained on fully-annotated data currently yield state o...
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Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
The dominant object detection approaches treat each dataset separately a...
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Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Multi-objective Neural Architecture Search (NAS) aims to discover novel ...
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MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly ...
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SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection
The state-of-the-art object detection method is complicated with various...
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DARTS+: Improved Differentiable Architecture Search with Early Stopping
Recently, there has been a growing interest in automating the process of...
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Meta Reinforcement Learning with Task Embedding and Shared Policy
Despite significant progress, deep reinforcement learning (RL) suffers f...
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Meta-Learning for Few-shot Camera-Adaptive Color Constancy
Digital camera pipelines employ color constancy methods to estimate an u...
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DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is cri...
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Federated Meta-Learning for Recommendation
Recommender systems have been widely studied from the machine learning p...
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Deep Meta-Learning: Learning to Learn in the Concept Space
Few-shot learning remains challenging for meta-learning that learns a le...
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is cri...
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