3 citations · 5 across the 2 of their papers we have counts for
6 papers
Patch-based Brain Age Estimation from MR Images
Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson +2
Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. This is a potential biomarker…
Biologically inspired architectures for sample-efficient deep reinforcement learning
Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we u…
How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?
Akhil Gupta, Naman Shukla, Lavanya Marla +2
The importance of domain knowledge in enhancing model performance and making reliable predictions in the real-world is critical. This has led to an increased focus on specific mode…
Adaptive Model Selection Framework: An Application to Airline Pricing
Naman Shukla, Arinbjörn Kolbeinsson, Lavanya Marla +1
Multiple machine learning and prediction models are often used for the same prediction or recommendation task. In our recent work, where we develop and deploy airline ancillary pri…
Dynamic Pricing for Airline Ancillaries with Customer Context
Naman Shukla, Arinbjörn Kolbeinsson, Ken Otwell +2
Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimiz…
Tensor Dropout for Robust Learning
Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis +4
CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets. However, they have limited generalization ability to data outsid…