6 papers
Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang +5
Vector search, the task of finding the k-nearest neighbors of a query vector against a database of high-dimensional vectors, underpins many machine learning applications, including…
MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage
Yongjun He, Roger Waleffe, Zhichao Han +8
Many modern machine learning (ML) methods rely on embedding models to learn vector representations (embeddings) for a set of entities (embedding tables). As increasingly diverse ML…
Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
Roger Waleffe, Devesh Sarda, Jason Mohoney +3
We study distributed training of Graph Neural Networks (GNNs) on billion-scale graphs that are partitioned across machines. Efficient training in this setting relies on min-edge-cu…
Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations
Cedric Renggli, Ihab F. Ilyas, Theodoros Rekatsinas
In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metric…
TSDS: Data Selection for Task-Specific Model Finetuning
Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
Finetuning foundation models for specific tasks is an emerging paradigm in modern machine learning. The efficacy of task-specific finetuning largely depends on the selection of app…
Incremental IVF Index Maintenance for Streaming Vector Search
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury +7
The prevalence of vector similarity search in modern machine learning applications and the continuously changing nature of data processed by these applications necessitate efficien…