59 citations · 66 across the 7 of their papers we have counts for
7 papers · 1 filter
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1
Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
Arshdeep Sekhon, Zhe Wang, Yanjun Qi
Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnost…
Neural Message Passing for Multi-Label Classification
Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul chall…
DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi
Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the funct…
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang, Arshdeep Sekhon, Yanjun Qi
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroi…
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh +1
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TF…