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Ahmed K. Farahat
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2020 – today
- 2023
- [j6]Jueming Hu
, Haiyan Wang, Hsiu-Khuern Tang, Takuya Kanazawa
Knowledge-enhanced reinforcement learning for multi-machine integrated production and maintenance scheduling. Comput. Ind. Eng. 185: 109631 (2023) - [c27]Xian Yeow Lee
XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection. CVPR Workshops 2023: 4375-4384 - [c26]Xian Yeow Lee
An ensemble of convolution-based methods for fault detection using vibration signals. ICPHM 2023: 172-179 - [i15]Nishant Yadav, Mahbubul Alam, Ahmed K. Farahat, Dipanjan Ghosh, Chetan Gupta, Auroop R. Ganguly:
CDA: Contrastive-adversarial Domain Adaptation. CoRR abs/2301.03826 (2023) - [i14]Xian Yeow Lee, Aman Kumar, Lasitha Vidyaratne, Aniruddha Rajendra Rao
An ensemble of convolution-based methods for fault detection using vibration signals. CoRR abs/2305.05532 (2023) - 2021
- [j5]Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Shuai Zheng:
Deep time series models for scarce data. Neurocomputing 456: 504-518 (2021) - [c25]Dustin Carrión-Ojeda, Mahbubul Alam, Sergio Escalera, Ahmed K. Farahat, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Chetan Gupta, Isabelle Guyon, Joël Roman Ky, Xian Yeow Lee, Xin Liu, Felix Mohr, Manh Hung Nguyen, Emmanuel Pintelas, Stefan Roth, Simone Schaub-Meyer, Haozhe Sun
NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned. NeurIPS (Competition and Demos) 2021: 50-72 - [i13]Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Shuai Zheng:
Deep Time Series Models for Scarce Data. CoRR abs/2103.09348 (2021) - [i12]Hamed Khorasgani, Haiyan Wang, Chetan Gupta, Ahmed K. Farahat:
An Offline Deep Reinforcement Learning for Maintenance Decision-Making. CoRR abs/2109.15050 (2021) - [i11]Hamed Khorasgani, Ahmed K. Farahat, Chetan Gupta:
Data-driven Residual Generation for Early Fault Detection with Limited Data. CoRR abs/2110.15385 (2021) - 2020
- [c24]Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Haiyan Wang:
Health Indicator Forecasting for Improving Remaining Useful Life Estimation. ICPHM 2020: 1-8 - [c23]Chetan Gupta, Ahmed K. Farahat:
Deep Learning for Industrial AI: Challenges, New Methods and Best Practices. KDD 2020: 3571-3572 - [c22]Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Ahmed K. Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav V. Marathe:
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. NeurIPS 2020 - [i10]Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Haiyan Wang:
Health Indicator Forecasting for Improving Remaining Useful Life Estimation. CoRR abs/2006.03729 (2020) - [i9]Lijing Wang
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. CoRR abs/2011.06796 (2020)
2010 – 2019
- 2019
- [c21]Chi Zhang, Chetan Gupta, Seiji Joichi, Ahmed K. Farahat, Huijuan Shao:
Risk-Based Dynamic Pricing via Failure Prediction. ICMLA 2019: 140-147 - [c20]Qiyao Wang, Ahmed K. Farahat, Kosta Ristovski, Chetan Gupta, Shuai Zheng:
Evaluation of Event Impact on Key Performance Indicators. ICMLA 2019: 726-733 - [c19]Hamed Khorasgani, Arman Hasanzadeh, Ahmed K. Farahat, Chetan Gupta:
Fault Detection and Isolation in Industrial Networks using Graph Convolutional Neural Networks. ICPHM 2019: 1-7 - [c18]Qiyao Wang, Shuai Zheng, Ahmed K. Farahat, Susumu Serita, Chetan Gupta:
Remaining Useful Life Estimation Using Functional Data Analysis. ICPHM 2019: 1-8 - [c17]Qiyao Wang, Shuai Zheng, Ahmed K. Farahat, Susumu Serita, Takashi Saeki, Chetan Gupta:
Multilayer Perceptron for Sparse Functional Data. IJCNN 2019: 1-10 - [c16]Shuai Zheng, Ahmed K. Farahat, Chetan Gupta:
Generative Adversarial Networks for Failure Prediction. ECML/PKDD (3) 2019: 621-637 - [i8]Qiyao Wang, Shuai Zheng, Ahmed K. Farahat, Susumu Serita, Chetan Gupta:
Remaining Useful Life Estimation Using Functional Data Analysis. CoRR abs/1904.06442 (2019) - [i7]Shuai Zheng, Ahmed K. Farahat, Chetan Gupta:
Generative Adversarial Networks for Failure Prediction. CoRR abs/1910.02034 (2019) - 2018
- [c15]Karan Aggarwal, Onur Atan, Ahmed K. Farahat, Chi Zhang, Kosta Ristovski, Chetan Gupta:
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling. IEEE BigData 2018: 1308-1317 - [c14]Qiyao Wang, Ahmed K. Farahat, Kosta Ristovski, Hsiu-Khuern Tang, Susumu Serita, Chetan Gupta:
What Maintenance is Worth the Money? A Data-Driven Answer. INDIN 2018: 284-291 - [c13]Chi Zhang, Chetan Gupta, Ahmed K. Farahat, Kosta Ristovski, Dipanjan Ghosh:
Equipment Health Indicator Learning Using Deep Reinforcement Learning. ECML/PKDD (3) 2018: 488-504 - [i6]Karan Aggarwal, Onur Atan, Ahmed K. Farahat, Chi Zhang, Kosta Ristovski, Chetan Gupta:
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling. CoRR abs/1812.07142 (2018) - 2017
- [c12]Shuai Zheng, Kosta Ristovski, Ahmed K. Farahat, Chetan Gupta:
Long Short-Term Memory Network for Remaining Useful Life estimation. ICPHM 2017: 88-95 - 2016
- [j4]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel:
Detecting emerging and evolving novelties with locally adaptive density ratio estimation. Knowl. Inf. Syst. 49(3): 1131-1159 (2016) - 2015
- [j3]Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi, Mohamed S. Kamel:
Greedy column subset selection for large-scale data sets. Knowl. Inf. Syst. 45(1): 1-34 (2015) - [c11]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel:
Ensemble Kernel Mean Matching. ICDM 2015: 330-338 - [c10]Ahmed Elbagoury, Rania Ibrahim, Ahmed K. Farahat, Mohamed S. Kamel, Fakhri Karray:
Exemplar-Based Topic Detection in Twitter Streams. ICWSM 2015: 610-613 - [c9]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel:
Locally Adaptive Density Ratio for Detecting Novelty in Twitter Streams. WWW (Companion Volume) 2015: 799-804 - [c8]Sepideh Seifzadeh, Ahmed K. Farahat, Mohamed S. Kamel, Fakhri Karray:
Short-Text Clustering using Statistical Semantics. WWW (Companion Volume) 2015: 805-810 - [i5]Mehrdad J. Gangeh, Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel:
Supervised Dictionary Learning and Sparse Representation-A Review. CoRR abs/1502.05928 (2015) - 2014
- [c7]Ahmed Elgohary, Ahmed K. Farahat, Mohamed S. Kamel, Fakhri Karray:
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce. SDM 2014: 425-433 - [c6]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel:
Discriminative Density-ratio Estimation. SDM 2014: 830-838 - 2013
- [j2]Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel
Efficient greedy feature selection for unsupervised learning. Knowl. Inf. Syst. 35(2): 285-310 (2013) - [c5]Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi, Mohamed S. Kamel
Distributed Column Subset Selection on MapReduce. ICDM 2013: 171-180 - [c4]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel
Auto-Tuning Kernel Mean Matching. ICDM Workshops 2013: 560-567 - [i4]Ahmed Elgohary, Ahmed K. Farahat, Mohamed S. Kamel, Fakhri Karray:
Embed and Conquer: Scalable Embeddings for Kernel $k$-Means on MapReduce. CoRR abs/1311.2334 (2013) - [i3]Yun-Qian Miao, Ahmed K. Farahat, Mohamed S. Kamel:
Discriminative Density-ratio Estimation. CoRR abs/1311.4486 (2013) - [i2]Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel:
A Fast Greedy Algorithm for Generalized Column Subset Selection. CoRR abs/1312.6820 (2013) - [i1]Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi, Mohamed S. Kamel:
Greedy Column Subset Selection for Large-scale Data Sets. CoRR abs/1312.6838 (2013) - 2011
- [j1]Ahmed K. Farahat, Mohamed S. Kamel
Statistical semantics for enhancing document clustering. Knowl. Inf. Syst. 28(2): 365-393 (2011) - [c3]Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel
An Efficient Greedy Method for Unsupervised Feature Selection. ICDM 2011: 161-170 - [c2]Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel:
A novel greedy algorithm for Nyström approximation. AISTATS 2011: 269-277
2000 – 2009
- 2009
- [c1]Ahmed K. Farahat, Mohamed S. Kamel
Document Clustering Using Semantic Kernels Based on Term-Term Correlations. ICDM Workshops 2009: 459-464
Coauthor Index
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