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Found 12 papers, 4 papers with code
Harmless Transfer Learning for Item Embeddings
On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred.
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
In this work, we explore a "pre-train, and search" paradigm for efficient sharding.
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error.
AutoShard: Automated Embedding Table Sharding for Recommender Systems
This is a significant design challenge of distributed systems named embedding table sharding, i. e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard.
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
We show that our general performance model not only achieves low prediction error on DLRM, which has highly customized configurations and is dominated by multiple factors but also yields comparable accuracy on other compute-bound ML models targeted by most previous methods.
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
To the best of our knowledge, this is the first work to provide in-depth analysis and discussion of applying pruning to online recommendation systems with non-stationary data distribution.
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data
Further, the lack of characterization of performance -- both with respect to real-timeliness and accuracy -- on production data sets makes model selection very challenging.
Real Time Analytics: Algorithms and Systems
Velocity is one of the 4 Vs commonly used to characterize Big Data.
Automatic Anomaly Detection in the Cloud Via Statistical Learning
Although there exists a large body of prior research in anomaly detection, existing techniques are not applicable in the context of social network data, owing to the inherent seasonal and trend components in the time series data.
