no code implementations • SEMEVAL 2018 • Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko
We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet.
no code implementations • COLING 2018 • Mohammad Salameh, Houda Bouamor, Nizar Habash
Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification).
no code implementations • LREC 2018 • Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alex Erdmann, er, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Esk, Ramy er, Mohammad Salameh, Hind Saddiki
no code implementations • NAACL 2019 • Ossama Obeid, Mohammad Salameh, Houda Bouamor, Nizar Habash
This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text.
no code implementations • LREC 2016 • Saif Mohammad, Mohammad Salameh, Svetlana Kiritchenko
Existing Arabic sentiment lexicons have low coverage―with only a few thousand entries.
no code implementations • 1 Sep 2020 • Tong Mo, Yakun Yu, Mohammad Salameh, Di Niu, Shangling Jui
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.
Ranked #1 on Keyword Spotting on Google Speech Commands (Google Speech Commands V1 6 metric)
no code implementations • 1 Jan 2021 • SEYED SAEED CHANGIZ REZAEI, Fred X. Han, Di Niu, Mohammad Salameh, Keith G Mills, Shangling Jui
Despite the empirical success of neural architecture search (NAS) algorithms in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to be assessed.
no code implementations • 19 May 2021 • SEYED SAEED CHANGIZ REZAEI, Fred X. Han, Di Niu, Mohammad Salameh, Keith Mills, Shuo Lian, Wei Lu, Shangling Jui
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess.
no code implementations • 25 Sep 2021 • Keith G. Mills, Fred X. Han, Mohammad Salameh, SEYED SAEED CHANGIZ REZAEI, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu
In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.
no code implementations • 29 Sep 2021 • Fred X. Han, Fabian Chudak, Keith G Mills, Mohammad Salameh, Parsa Riahi, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
no code implementations • 20 Nov 2021 • Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning.
Autonomous Driving Explainable Artificial Intelligence (XAI) +1
no code implementations • 21 Dec 2021 • Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving.
no code implementations • 21 Feb 2023 • Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
no code implementations • 26 Jan 2024 • Amirhosein Ghasemabadi, Mohammad Salameh, Muhammad Kamran Janjua, Chunhua Zhou, Fengyu Sun, Di Niu
Image restoration tasks traditionally rely on convolutional neural networks.
Ranked #1 on Image Denoising on SIDD
no code implementations • 18 Mar 2024 • Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices.
no code implementations • 10 Apr 2024 • Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles.
1 code implementation • 20 Mar 2024 • Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu
Neural Architecture Search is a costly practice.
1 code implementation • 5 Mar 2023 • Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh, Jialin Zhang, Wei Lui, Shangling Jui, Di Niu
Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training.
1 code implementation • 30 Nov 2022 • Keith G. Mills, Fred X. Han, Jialin Zhang, Fabian Chudak, Ali Safari Mamaghani, Mohammad Salameh, Wei Lu, Shangling Jui, Di Niu
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
1 code implementation • 30 Nov 2022 • Keith G. Mills, Di Niu, Mohammad Salameh, Weichen Qiu, Fred X. Han, Puyuan Liu, Jialin Zhang, Wei Lu, Shangling Jui
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
1 code implementation • 19 Jul 2023 • Shahin Atakishiyev, Mohammad Salameh, Housam Babiker, Randy Goebel
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms.