4 citations · 8 across the 4 of their papers we have counts for
4 papers
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 Compression and 3.1 Faster Inference
Aditya Desai, Li Chou, Anshumali Shrivastava
Deep learning for recommendation data is one of the most pervasive and challenging AI workload in recent times. State-of-the-art recommendation models are one of the largest models…
Efficient and Less Centralized Federated Learning
Li Chou, Zichang Liu, Zhuang Wang +1
With the rapid growth in mobile computing, massive amounts of data and computing resources are now located at the edge. To this end, Federated learning (FL) is becoming a widely ad…
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems
Aditya Desai, Yanzhou Pan, Kuangyuan Sun +2
Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical…
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking
Zichang Liu, Li Chou, Anshumali Shrivastava
On-device localization and tracking are increasingly crucial for various applications. Along with a rapidly growing amount of location data, machine learning (ML) techniques are be…