59 citations · 86 across the 5 of their papers we have counts for
8 papers
General Multi-label Image Classification with Transformers
Jack Lanchantin, Tianlu Wang, Vicente Ordonez +1
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the C…
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…
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Jack Lanchantin, Ji Gao
Statistical language models are powerful tools which have been used for many tasks within natural language processing. Recently, they have been used for other sequential data such…
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…
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon +1
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation…
Memory Matching Networks for Genomic Sequence Classification
Jack Lanchantin, Ritambhara Singh, Yanjun Qi
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manual…