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Found 17 papers, 5 papers with code
Pricing and Competition for Generative AI
We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness.
Uncertainty Estimation for 3D Object Detection via Evidential Learning
We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector.
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Inspired by this observation, we develop a zero-shot prompting technique, SpatialPrompt, that encourages VLMs to answer quantitative spatial questions using reference objects as visual cues.
No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data
In this paper, we present a framework that enables quality checking of visual data at large scales without sacrificing the reliability of the results.
AutoScale: Automatic Prediction of Compute-optimal Data Composition for Training LLMs
Domain reweighting is an emerging research area aimed at adjusting the relative weights of different data sources to improve the effectiveness and efficiency of language model pre-training.
Can Feedback Enhance Semantic Grounding in Large Vision-Language Models?
We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs.
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain.
Optimizing Data Collection for Machine Learning
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect.
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance?
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label.
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label.
OpenKBP: The open-access knowledge-based planning grand challenge
The purpose of this work is to advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research.
The importance of evaluating the complete automated knowledge-based planning pipeline
We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans.
Knowledge-based automated planning with three-dimensional generative adversarial networks
Our pipeline consisted of a generative adversarial network (GAN) to predict dose from a CT image followed by two optimization models to learn objective function weights and generate fluence-based plans, respectively.
Medical Physics
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones.
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities.
