Search Results for author:
Found 9 papers, 4 papers with code
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems
In this paper, we propose a Retrieval Augmented Generation (RAG) agent that maps natural language queries about research topics to precise, machine-interpretable semantic entities.
SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches
In this paper, we will present SketchQL, a video database management system (VDBMS) for retrieving video moments with a sketch-based query interface.
Dynamic Data Layout Optimization with Worst-case Guarantees
In this paper, we present an algorithmic framework OReO that makes online reorganization decisions to balance the benefits of improved query performance with the costs of reorganization.
Falcon: Fair Active Learning using Multi-armed Bandits
Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e. g., (attribute=female, label=positive)) that are the most informative for improving fairness.
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models.
Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data
In this vision paper, we propose a shift in perspective for improving the effectiveness of similarity search.
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage.
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance.
