1 citations · 1 across the 5 of their papers we have counts for
6 papers
Human Cognition in Machines: A Unified Perspective of World Models
Timothy Rupprecht, Pu Zhao, Amir Taherin +20
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To…
SSplain: Sparse and Smooth Explainer for Retinopathy of Prematurity Classification
Elifnur Sunger, Tales Imbiriba, Peter Campbell +3
Neural networks are frequently used in medical diagnosis. However, due to their black-box nature, model explainers are used to help clinicians understand better and trust model out…
On the Role of Calibration in Benchmarking Algorithmic Fairness for Skin Cancer Detection
Brandon Dominique, Prudence Lam, Nicholas Kurtansky +4
Artificial Intelligence (AI) models have demonstrated expert-level performance in melanoma detection, yet their clinical adoption is hindered by performance disparities across demo…
H-SPLID: HSIC-based Saliency Preserving Latent Information Decomposition
Lukas Miklautz, Chengzhi Shi, Andrii Shkabrii +5
We introduce H-SPLID, a novel algorithm for learning salient feature representations through the explicit decomposition of salient and non-salient features into separate spaces. We…
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
Ziniu Zhang, Zhenshuo Zhang, Dongyue Li +3
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of examples, how can we quickly select out of to…
Dependency-aware Maximum Likelihood Estimation for Active Learning
Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis +2
Active learning aims to efficiently build a labeled training set by strategically selecting samples to query labels from annotators. In this sequential process, each sample acquisi…