| Attend and predict: Understanding gene regulation by selective attention on chromatin R Singh, J Lanchantin, A Sekhon, Y Qi Advances in neural information processing systems 30, 2017 | 97 | 2017 |
| DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications A Sekhon, R Singh, Y Qi Bioinformatics 34 (17), i891-i900, 2018 | 69 | 2018 |
| Neural message passing for multi-label classification J Lanchantin, A Sekhon, Y Qi Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 48 | 2020 |
| Perturbing inputs for fragile interpretations in deep natural language processing S Sinha, H Chen, A Sekhon, Y Ji, Y Qi arXiv preprint arXiv:2108.04990, 2021 | 42 | 2021 |
| GaKCo: A Fast Gapped k-mer String Kernel Using Counting R Singh, A Sekhon, K Kowsari, J Lanchantin, B Wang, Y Qi Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 35 | 2017 |
| Transfer learning for predicting virus-host protein interactions for novel virus sequences J Lanchantin, T Weingarten, A Sekhon, C Miller, Y Qi Proceedings of the 12th ACM International Conference on Bioinformatics …, 2021 | 15 | 2021 |
| Does Prompt Formatting Have Any Impact on LLM Performance? J He, M Rungta, D Koleczek, A Sekhon, FX Wang, S Hasan arXiv preprint arXiv:2411.10541, 2024 | 13 | 2024 |
| Fast and scalable learning of sparse changes in high-dimensional gaussian graphical model structure B Wang, Y Qi International Conference on Artificial Intelligence and Statistics, 1691-1700, 2018 | 9 | 2018 |
| ST-MAML: A stochastic-task based method for task-heterogeneous meta-learning Z Wang, J Grigsby, A Sekhon, Y Qi Uncertainty in Artificial Intelligence, 2066-2074, 2022 | 7 | 2022 |
| Evolving image compositions for feature representation learning P Cascante-Bonilla, A Sekhon, Y Qi, V Ordonez arXiv preprint arXiv:2106.09011, 2021 | 7 | 2021 |
| A fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian graphical models B Wang, A Sekhon, Y Qi International Conference on Machine Learning, 5161-5170, 2018 | 6 | 2018 |
| Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification J Lanchantin, A Sekhon, R Singh, Y Qi arXiv preprint arXiv:1710.11238, 2017 | 6 | 2017 |
| Improving interpretability via explicit word interaction graph layer A Sekhon, H Chen, A Shrivastava, Z Wang, Y Ji, Y Qi Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 13528 …, 2023 | 5 | 2023 |
| Transfer learning with motiftrans-formers for predicting protein-protein interactions between a novel virus and humans J Lanchantin, A Sekhon, C Miller, Y Qi BioRxiv 36, i659-i667, 2020 | 4 | 2020 |
| White-box Testing of NLP models with Mask Neuron Coverage A Sekhon, Y Ji, MB Dwyer, Y Qi arXiv preprint arXiv:2205.05050, 2022 | 2 | 2022 |
| Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG A Sekhon, Z Wang, Y Qi arXiv preprint arXiv:2103.02405, 2021 | 1 | 2021 |
| Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge A Sekhon, Z Wang, Y Qi International Conference on Artificial Intelligence and Statistics, 10881-10923, 2022 | | 2022 |
| Differential Network Learning Beyond Data Samples. A Sekhon, B Wang, Z Wang, Y Qi CoRR, 2020 | | 2020 |
| Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models A Sekhon, B Wang, Y Qi bioRxiv, 716852, 2019 | | 2019 |
| JointNets: an End-to-end R package for sparse Gaussian graphical model Z Wang, B Wang, A Sekhon, Y Qi | | 2019 |