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Found 6 papers, 3 papers with code
Differentiable NAS Framework and Application to Ads CTR Prediction
Neural architecture search (NAS) methods aim to automatically find the optimal deep neural network (DNN) architecture as measured by a given objective function, typically some combination of task accuracy and inference efficiency.
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models
While Check-N-Run is applicable to long running ML jobs, we focus on checkpointing recommendation models which are currently the largest ML models with Terabytes of model size.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM.
Glow: Graph Lowering Compiler Techniques for Neural Networks
The high-level intermediate representation allows the optimizer to perform domain-specific optimizations.
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