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Found 9 papers, 5 papers with code
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
Sobolev Independence Criterion
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.
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection
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.
Wasserstein Barycenter Model Ensembling
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.
Improved Neural Relation Detection for Knowledge Base Question Answering
Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA).
Attentive Pooling Networks
In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.
Ranked #2 on
Question Answering
on SemEvalCQA
LSTM-based Deep Learning Models for Non-factoid Answer Selection
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.
