125 citations · 222 across the 16 of their papers we have counts for
16 papers
Uncertainty Estimation for 3D Object Detection via Evidential Learning
Nikita Durasov, Rafid Mahmood, Jiwoong Choi +4
3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, lea…
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler +1
Despite recent advances demonstrating vision-language models' (VLMs) abilities to describe complex relationships in images using natural language, their capability to quantitativel…
Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?
Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler +1
Enhancing semantic grounding abilities in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying th…
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu, David Acuna, Andrew Liao +5
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 domai…
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez +2
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. Over-collecting dat…
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Rafid Mahmood, James Lucas, David Acuna +6
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…
Inverse Optimization: Theory and Applications
Timothy C. Y. Chan, Rafid Mahmood, Ian Yihang Zhu
Inverse optimization describes a process that is the "reverse" of traditional mathematical optimization. Unlike traditional optimization, which seeks to compute optimal decisions g…
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Rafid Mahmood, Sanja Fidler, Marc T. Law
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. The large scale of data sets used in dee…
OpenKBP: The open-access knowledge-based planning grand challenge
Aaron Babier, Binghao Zhang, Rafid Mahmood +4
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. We hosted OpenKB…
The importance of evaluating the complete automated knowledge-based planning pipeline
Aaron Babier, Rafid Mahmood, Andrea L. McNiven +2
We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained…
Knowledge-based automated planning with three-dimensional generative adversarial networks
Aaron Babier, Rafid Mahmood, Andrea L. McNiven +2
We develop a knowledge-based automated planning (KBAP) pipeline that generates treatment plans using deep neural network architectures for predicting 3D doses. Our pipeline consist…
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Rafid Mahmood, Aaron Babier, Andrea McNiven +2
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…
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
Aaron Babier, Timothy C. Y. Chan, Adam Diamant +1
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this work, we combine techniques f…
An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization
Aaron Babier, Timothy C. Y. Chan, Taewoo Lee +2
We develop a generalized inverse optimization framework for fitting the cost vector of a single linear optimization problem given multiple observed decisions. This setting is motiv…
Convolutional Codes with Maximum Column Sum Rank for Network Streaming
Rafid Mahmood, Ahmed Badr, Ashish Khisti
The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known a…