ALT: An Automatic System for Long Tail Scenario Modeling
Not on the map. Why?
Abstract (arXiv)
In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and computing resources for model inference stage. This problem is widely encountered in various applications, yet has received deficient attention so far. We present an automatic system named ALT to deal with this problem. Several efforts are taken to improve the algorithms used in our system, such as employing various automatic machine learning related techniques, adopting the meta learning philosophy, and proposing an essential budget-limited neural architecture search method, etc. Moreover, to build the system, many optimizations are performed from a systematic perspective, and essential modules are armed, making the system more feasible and efficient. We perform abundant experiments to validate the effectiveness of our system and demonstrate the usefulness of the critical modules in our system. Moreover, online results are provided, which fully verified the efficacy of our system.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 71 references that are in the map. First posted on arXiv 19 May 2023.
Builds on
30 of 71 references.
References · 30
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2018DARTS: Differentiable Architecture Searchcited by 1,798
- 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
- 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
- 2016Learning to learn by gradient descent by gradient descentcited by 620
- 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 470
- 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
- 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
18 more
- 2018SNAS: Stochastic Neural Architecture Searchcited by 289
- 2019Adversarial Examples Improve Image Recognitioncited by 286
- 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 260
- 2019MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendationcited by 121
- 2021One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Predictioncited by 78
- 2019Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddingscited by 66
- 2018Automated Machine Learning: From Principles to Practicescited by 62
- 2020AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Searchcited by 51
- 2020Semi-Supervised Neural Architecture Searchcited by 46
- 2021Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networkscited by 37
- 2017Hyperparameter Optimization: A Spectral Approachcited by 24
- 2021A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Appscited by 21
- 2020SAFE: Scalable Automatic Feature Engineering Framework for Industrial Taskscited by 17
- 2021Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendationcited by 12
- 2021MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Datacited by 9
- 2018Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraudcited by 7
- 2021We Know What You Want: An Advertising Strategy Recommender System for Online Advertisingcited by 5
- 2020How Much Can A Retailer Sell? Sales Forecasting on Tmallcited by 3
Built on by
Not cited yet.