10 citations · 22 across the 24 of their papers we have counts for
5 papers · 1 filter
Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Zhenxin Li, Nadine Chang, Xinglong Sun +8
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implaus…
Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
Nadine Chang, Maying Shen, Shizhe Diao +6
Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, standard data curation methods…
From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence
Nadine Chang, Maying Shen, Shizhe Diao +6
We propose a language representation for multimodal data in which any observation, whether image, video, or text, is expressed as a bag of atomic propositions, simple statements ab…
Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
Nadine Chang, Maying Shen, Jialiang Wang +2
Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system maintenance pipelines use a reactive…
Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
Tolga Dimlioglu, Nadine Chang, Maying Shen +2
Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, the training data, must address…