2 code implementations • 22 May 2020 • Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, Aleksandra Mojsilovic
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
In the kernel version we show that SIC can be cast as a convex optimization problem by introducing auxiliary variables that play an important role in feature selection as they are normalized feature importance scores.
We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task.
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.
Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA).
In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.
Ranked #2 on Question Answering on SemEvalCQA
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.