5 citations · 5 across the 2 of their papers we have counts for
3 papers
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
Zihao Deng, Benjamin Ghaemmaghami, Ashish Kumar Singh +4
Modern DNN-based recommendation systems rely on training-derived embeddings of sparse features. Input sparsity makes obtaining high-quality embeddings for rarely-occurring categori…
Harvesting Brownian Motion: Zero Energy Computational Sampling
David Doty, Niels Kornerup, Austin Luchsinger +3
The key factor currently limiting the advancement of computational power of electronic computation is no longer the manufacturing density and speed of components, but rather their…
Training with Multi-Layer Embeddings for Model Reduction
Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho +4
Modern recommendation systems rely on real-valued embeddings of categorical features. Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost…