Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (see more)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
Systems consolidation impairs behavioral flexibility
Sankirthana Sathiyakumar
Sofia Skromne Carrasco
Lydia Saad
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage
Shahar Avin
Jasmine Wang
Haydn Belfield
Gretchen Krueger
Gillian K. Hadfield
Heidy Khlaaf
Jingying Yang
H. Toner
Ruth Catherine Fong
Pang Wei Koh
Sara Hooker
Jade Leung
Andrew John Trask
Emma Bluemke
Jonathan Lebensbold
Cullen C. O'keefe
Mark Koren
Th'eo Ryffel … (see 39 more)
JB Rubinovitz
Tamay Besiroglu
Federica Carugati
Jack Clark
Peter Eckersley
Sarah de Haas
Maritza L. Johnson
Ben Laurie
Alex Ingerman
Igor Krawczuk
Amanda Askell
Rosario Cammarota
A. Lohn
Charlotte Stix
Peter Mark Henderson
Logan Graham
Carina E. A. Prunkl
Bianca Martin
Elizabeth Seger
Noa Zilberman
Sean O hEigeartaigh
Frens Kroeger
Girish Sastry
R. Kagan
Adrian Weller
Brian Shek-kam Tse
Elizabeth Barnes
Allan Dafoe
Paul D. Scharre
Ariel Herbert-Voss
Martijn Rasser
Shagun Sodhani
Carrick Flynn
Thomas Krendl Gilbert
Lisa Dyer
Saif M. Khan
Markus Anderljung
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Müller
Gonzalo Muñoz
Ambros Gleixner
Andrea Lodi
Felipe Serrano
Clustering for Continuous-Time Hidden Markov Models.
Yu Luo
David A. Stephens
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized line… (see more)ar observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
Establishing an evaluation metric to quantify climate change image realism
Sharon Zhou
Alexandra Luccioni
Gautier Cosne
Michael S. Bernstein
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (see more)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Detecting semantic anomalies
Faruk Ahmed
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmark… (see more)s. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.
Gifting in Multi-Agent Reinforcement Learning (Student Abstract)
Andrei-Stefan Lupu
This work performs a first study on multi-agent reinforcement learning with deliberate reward passing between agents. We empirically demonst… (see more)rate that such mechanics can greatly improve the learning progression in a resource appropriation setting and provide a preliminary discussion of the complex effects of gifting on the learning dynamics.
Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach
Sahil Garg
Guillermo Cecchi
Palash Goyal
Shuyang Gao
Sarik Ghazarian
Greg Ver Steeg
Aram Galstyan
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which le… (see more)arns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.