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Aamer Abdul Rahman

aamer.abdul-rahman@mila.quebec
Maîtrise recherche - École de technologie suprérieure
Superviseur⋅e principal⋅e
Samira Ebrahimi Kahou
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Publications

Empowering Clinicians with MeDT: A Framework for Sepsis Treatment
Aamer Abdul Rahman
Pranav Agarwal
Vincent Michalski
Rita Noumeir
Samira Ebrahimi Kahou
2023-11-02
NeurIPS.cc/2023/Workshop/GCRL (publié)
openreview.net
openreview.net
Transformers in Reinforcement Learning: A Survey
Pranav Agarwal
Aamer Abdul Rahman
Pierre-Luc St-Charles
Simon J. D. Prince
Samira Ebrahimi Kahou
Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve perform… (voir plus)ance compared to other neural networks. This survey explores how transformers are used in reinforcement learning (RL), where they are seen as a promising solution for addressing challenges such as unstable training, credit assignment, lack of interpretability, and partial observability. We begin by providing a brief domain overview of RL, followed by a discussion on the challenges of classical RL algorithms. Next, we delve into the properties of the transformer and its variants and discuss the characteristics that make them well-suited to address the challenges inherent in RL. We examine the application of transformers to various aspects of RL, including representation learning, transition and reward function modeling, and policy optimization. We also discuss recent research that aims to enhance the interpretability and efficiency of transformers in RL, using visualization techniques and efficient training strategies. Often, the transformer architecture must be tailored to the specific needs of a given application. We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization. We conclude by discussing the limitations of using transformers in RL and assess their potential for catalyzing future breakthroughs in this field.
2023-07-12
ArXiv (prépublication)
doi.org
arxiv.org
Learning from uncertain concepts via test time interventions
Ivaxi Sheth
Aamer Abdul Rahman
Laya Rafiee Sevyeri
Mohammad Havaei
Samira Ebrahimi Kahou
With neural networks applied to safety-critical applications, it has become increasingly important to understand the defining features of de… (voir plus)cision-making. Therefore, the need to uncover the black boxes to rational representational space of these neural networks is apparent. Concept bottleneck model (CBM) encourages interpretability by predicting human-understandable concepts. They predict concepts from input images and then labels from concepts. Test time intervention, a salient feature of CBM, allows for human-model interactions. However, these interactions are prone to information leakage and can often be ineffective inappropriate communication with humans. We propose a novel uncertainty based strategy, \emph{SIUL: Single Interventional Uncertainty Learning} to select the interventions. Additionally, we empirically test the robustness of CBM and the effect of SIUL interventions under adversarial attack and distributional shift. Using SIUL, we observe that the interventions suggested lead to meaningful corrections along with mitigation of concept leakage. Extensive experiments on three vision datasets along with a histopathology dataset validate the effectiveness of our interventional learning.
2022-11-20
NeurIPS.cc/2022/Workshop/TSRML (accepté)
openreview.net
openreview.net
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Moslem Yazdanpanah
Aamer Abdul Rahman
Muawiz Chaudhary
Christian Desrosiers
Mohammad Havaei
Eugene Belilovsky
Samira Ebrahimi Kahou
Batch normalization is a staple of computer vision models, including those employed in few-shot learning. Batch nor-malization layers in con… (voir plus)volutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org