Adel Bibi
PhD student at KAUST.
Recent work provides promising evidence that Physics-informed neural net...
We revisit the common practice of evaluating adaptation of Online Contin...
Improving and guaranteeing the robustness of deep learning models has be...
In this paper we investigate the frequency sensitivity of Deep Neural
Ne...
Continual Learning (CL) aims to sequentially train models on streams of
...
Current evaluations of Continual Learning (CL) methods typically assume ...
Continual Learning is a step towards lifelong intelligence where models
...
Deep learning models for vision tasks are trained on large datasets unde...
Autonomous intelligent agents deployed to the real-world need to be robu...
Despite clear computational advantages in building robust neural network...
Recently, Wong et al. showed that adversarial training with single-step ...
Randomized smoothing has recently emerged as an effective tool that enab...
Deep neural networks are vulnerable to input deformations in the form of...
Deep neural networks are vulnerable to small input perturbations known a...
Randomized smoothing is a recent technique that achieves state-of-art
pe...
The impressive performance of deep neural networks (DNNs) has immensely
...
This paper studies how encouraging semantically-aligned features during ...
This work tackles the problem of characterizing and understanding the
de...
This work takes a step towards investigating the benefits of merging
cla...
In this work, we study constrained clustering, where constraints are uti...
Training Deep Neural Networks (DNNs) that are robust to norm bounded
adv...
Despite the impressive performance of deep neural networks (DNNs) on num...
Despite the numerous developments in object tracking, further developmen...