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07/12/2023
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
Dividing ads ranking system into retrieval, early, and final stages is a...
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04/11/2023
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
Multi-task learning (MTL) aims at enhancing the performance and efficien...
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04/12/2021
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are used across many busines...
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11/05/2020
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
The paper proposes and optimizes a partial recovery training system, CPR...
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10/16/2020
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
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06/29/2020
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine...
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03/20/2020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Large-scale training is important to ensure high performance and accurac...
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03/07/2020
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Distributed training is useful to train complicated models to shorten th...
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11/05/2019
Post-Training 4-bit Quantization on Embedding Tables
Continuous representations have been widely adopted in recommender syste...
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09/25/2019
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
In many real-world applications, e.g. recommendation systems, certain it...
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09/04/2019
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Modern deep learning-based recommendation systems exploit hundreds to th...
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05/29/2019
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstratin...
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07/02/2016
Sub-sampled Newton Methods with Non-uniform Sampling
We consider the problem of finding the minimizer of a convex function F:...
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04/07/2015
Tensor machines for learning target-specific polynomial features
Recent years have demonstrated that using random feature maps can signif...
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02/12/2015
Weighted SGD for ℓ_p Regression with Randomized Preconditioning
In recent years, stochastic gradient descent (SGD) methods and randomize...
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02/10/2015
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
In this era of large-scale data, distributed systems built on top of clu...
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12/29/2014
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
We consider the problem of improving the efficiency of randomized Fourie...
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05/01/2013