Zobrazeno 1 - 10
of 1 887
pro vyhledávání: '"Erfani, P."'
Publikováno v:
Pattern Recognition, Vol. 156, 2024, Article No. 110758
High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional m
Externí odkaz:
http://arxiv.org/abs/2410.17741
The transportation sector significantly contributes to greenhouse gas emissions, highlighting the need to transition to Electric Vehicles (EVs) to reduce fossil fuel dependence and combat climate change. The US government has set ambitious targets fo
Externí odkaz:
http://arxiv.org/abs/2406.14295
It has recently been discovered that using a pre-trained vision-language model (VLM), e.g., CLIP, to align a whole query image with several finer text descriptions generated by a large language model can significantly enhance zero-shot performance. H
Externí odkaz:
http://arxiv.org/abs/2406.02915
Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing defensive measures can only mitigate less than half of these attacks at
Externí odkaz:
http://arxiv.org/abs/2402.13517
Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outputs. Shortcut learning is ubiquitous among many failure cases of neural
Externí odkaz:
http://arxiv.org/abs/2402.11237
Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learning (RL) are difficult to impl
Externí odkaz:
http://arxiv.org/abs/2402.04567
Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing noises that can fool a DNN model into believing that there is nothing (no
Externí odkaz:
http://arxiv.org/abs/2402.02028
Autor:
Huang, Hanxun, Campello, Ricardo J. G. B., Erfani, Sarah Monazam, Ma, Xingjun, Houle, Michael E., Bailey, James
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Externí odkaz:
http://arxiv.org/abs/2401.10474
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulate model behavior by embedding a hidden trigger during the training pha
Externí odkaz:
http://arxiv.org/abs/2401.03215
Autor:
Chang, Shuyu Y, Ghahremani, Zahra, Manuel, Laura, Erfani, Mohammad, Shen, Chaopeng, Cohen, Sagy, Van Meter, Kimberly, Pierce, Jennifer L, Meselhe, Ehab A, Goharian, Erfan
Hydraulic geometry parameters describing river hydrogeomorphic is important for flood forecasting. Although well-established, power-law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation wor
Externí odkaz:
http://arxiv.org/abs/2312.11476