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Arshdeep Sekhon
Arshdeep Sekhon
Applied Scientist @ Microsoft
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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
802017
DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications
A Sekhon, R Singh, Y Qi
Bioinformatics 34 (17), i891-i900, 2018
572018
Neural message passing for multi-label classification
J Lanchantin, A Sekhon, Y Qi
Machine Learning and Knowledge Discovery in Databases: European Conference ¡¦, 2020
372020
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
272017
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
222021
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
92018
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 Conference on Bioinformatics, Computational ¡¦, 2021
82021
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
62018
Evolving image compositions for feature representation learning
P Cascante-Bonilla, A Sekhon, Y Qi, V Ordonez
arXiv preprint arXiv:2106.09011, 2021
52021
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
52017
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 2020, 2020
42020
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
32022
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
12023
White-box Testing of NLP models with Mask Neuron Coverage
A Sekhon, Y Ji, MB Dwyer, Y Qi
arXiv preprint arXiv:2205.05050, 2022
12022
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
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
A Sekhon, Z Wang, Y Qi
arXiv preprint arXiv:2103.02405, 2021
2021
Transfer Learning with MotifTransformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans (preprint)
J Lanchantin, A Sekhon, CL Miller, Y Qi, N Sobh, E Valera, R Bashir, ...
2020
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
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