4 citations · 12 across the 5 of their papers we have counts for
5 papers
Aligning Dense Retrievers with LLM Utility via Distillation
Rajinder Sandhu, Di Mu, Cheng Chang +4
Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations. Conversely, utility-based ap…
Hyperparameter optimization with REINFORCE and Transformers
Chepuri Shri Krishna, Ashish Gupta, Swarnim Narayan +2
Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified T…
Learning Effective Visual Relationship Detector on 1 GPU
Yichao Lu, Cheng Chang, Himanshu Rai +2
We present our winning solution to the Open Images 2019 Visual Relationship challenge. This is the largest challenge of its kind to date with nearly 9 million training images. Chal…
Semi-Supervised Exploration in Image Retrieval
Cheng Chang, Himanshu Rai, Satya Krishna Gorti +4
We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2[9]. The goal was to retrieve all database imag…
Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering
Ga Wu, Maksims Volkovs, Chee Loong Soon +2
Previous highly scalable one-class collaborative filtering methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a la…