Deja vu: Contextual sparsity for efficient llms at inference time
Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …
wave of exciting AI applications. However, they are computationally expensive at inference …
Mongoose: A learnable lsh framework for efficient neural network training
Recent advances by practitioners in the deep learning community have breathed new life
into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in …
into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in …
Norm adjusted proximity graph for fast inner product retrieval
Efficient inner product search on embedding vectors is often the vital stage for online ranking
services, such as recommendation and information retrieval. Recommendation algorithms …
services, such as recommendation and information retrieval. Recommendation algorithms …
Reverse maximum inner product search: Formulation, algorithms, and analysis
The maximum inner product search (MIPS), which finds the item with the highest inner
product with a given query user, is an essential problem in the recommendation field …
product with a given query user, is an essential problem in the recommendation field …
[PDF][PDF] Recent Advances in Scalable Retrieval of Personalized Recommendations
Top-K recommendation seeks to deliver a personalized recommendation list of K items to a
user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer …
user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer …