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Found 8 papers, 4 papers with code
On the Diminishing Returns of Width for Continual Learning
While deep neural networks have demonstrated groundbreaking performance in various settings, these models often suffer from \emph{catastrophic forgetting} when trained on new tasks in sequence.
CAPS: A Practical Partition Index for Filtered Similarity Search
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest.
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions
Lookup tables are a fundamental structure in many data processing and systems applications.
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities.
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Low-Precision Quantization for Efficient Nearest Neighbor Search
Fast k-Nearest Neighbor search over real-valued vector spaces (KNN) is an important algorithmic task for information retrieval and recommendation systems.
Embracing Structure in Data for Billion-Scale Semantic Product Search
When training a dyadic model, one seeks to embed two different types of entities (e. g., queries and documents or users and movies) in a common vector space such that pairs with high relevance are positioned nearby.
A Fast Randomized Algorithm for Massive Text Normalization
In addition, FLAN does not require any annotated data or supervised learning.
Semantic Product Search
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
