| The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups C Curtis, SP Shah, SF Chin, G Turashvili, OM Rueda, MJ Dunning, ... Nature 486 (7403), 346-352, 2012 | 6430 | 2012 |
| The clonal and mutational evolution spectrum of primary triple-negative breast cancers SP Shah, A Roth, R Goya, A Oloumi, G Ha, Y Zhao, G Turashvili, J Ding, ... Nature 486 (7403), 395-399, 2012 | 2363 | 2012 |
| Graph-to-sequence learning using gated graph neural networks D Beck, G Haffari, T Cohn arXiv preprint arXiv:1806.09835, 2018 | 427 | 2018 |
| Iterative back-translation for neural machine translation CDV Hoang, P Koehn, G Haffari, T Cohn 2nd Workshop on Neural Machine Translation and Generation, 18-24, 2018 | 362 | 2018 |
| DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer A Bashashati, G Haffari, J Ding, G Ha, K Lui, J Rosner, DG Huntsman, ... Genome biology 13, 1-14, 2012 | 298 | 2012 |
| Reasoning on graphs: Faithful and interpretable large language model reasoning L Luo, YF Li, G Haffari, S Pan arXiv preprint arXiv:2310.01061, 2023 | 211 | 2023 |
| Selective attention for context-aware neural machine translation S Maruf, AFT Martins, G Haffari arXiv preprint arXiv:1903.08788, 2019 | 202 | 2019 |
| Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data J Ding, A Bashashati, A Roth, A Oloumi, K Tse, T Zeng, G Haffari, M Hirst, ... Bioinformatics 28 (2), 167-175, 2012 | 201 | 2012 |
| Incorporating structural alignment biases into an attentional neural translation model T Cohn, CDV Hoang, E Vymolova, K Yao, C Dyer, G Haffari arXiv preprint arXiv:1601.01085, 2016 | 200 | 2016 |
| Document context neural machine translation with memory networks S Maruf, G Haffari arXiv preprint arXiv:1711.03688, 2017 | 193 | 2017 |
| A latent variable recurrent neural network for discourse relation language models Y Ji, G Haffari, J Eisenstein arXiv preprint arXiv:1603.01913, 2016 | 179 | 2016 |
| PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy J Song, F Li, A Leier, TT Marquez-Lago, T Akutsu, G Haffari, KC Chou, ... Bioinformatics 34 (4), 684-687, 2018 | 160 | 2018 |
| A survey on document-level neural machine translation: Methods and evaluation S Maruf, F Saleh, G Haffari ACM Computing Surveys (CSUR) 54 (2), 1-36, 2021 | 157 | 2021 |
| Transductive learning for statistical machine translation N Ueffing, G Haffari, A Sarkar Proceedings of the 45th annual meeting of the association of computational …, 2007 | 140 | 2007 |
| PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework J Song, F Li, K Takemoto, G Haffari, T Akutsu, KC Chou, GI Webb Journal of theoretical biology 443, 125-137, 2018 | 137 | 2018 |
| Active learning for statistical phrase-based machine translation G Haffari, M Roy, A Sarkar Proceedings of Human Language Technologies: The 2009 Annual Conference of …, 2009 | 129 | 2009 |
| Active learning by feature mixing A Parvaneh, E Abbasnejad, D Teney, GR Haffari, A Van Den Hengel, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 124 | 2022 |
| Medical visual question answering: A survey Z Lin, D Zhang, Q Tao, D Shi, G Haffari, Q Wu, M He, Z Ge Artificial Intelligence in Medicine 143, 102611, 2023 | 121 | 2023 |
| Continual learning for large language models: A survey T Wu, L Luo, YF Li, S Pan, TT Vu, G Haffari arXiv preprint arXiv:2402.01364, 2024 | 119 | 2024 |
| Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning G Wan, S Pan, C Gong, C Zhou, G Haffari International Joint Conference on Artificial Intelligence, 2021 | 113 | 2021 |
Yuan-Fang LiOracle, Monash Universitymonash.edu의 이메일 확인됨
