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Found 8 papers, 0 papers with code
Robust Explainable Recommendation
In this work, we present a general framework for feature-aware explainable recommenders that can withstand external attacks and provide robust and generalized explanations.
Stability of Explainable Recommendation
Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease.
GAN based Data Augmentation to Resolve Class Imbalance
The number of credit card fraud has been growing as technology grows and people can take advantage of it.
Semantic Motion Correction Via Iterative Nonlinear Optimization and Animation
Here, we present an end-to-end method to create 2D animation for a goalkeeper attempting to block a penalty kick, and then correct that motion using an iterative nonlinear optimization scheme.
A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification
In this paper, we have also established that without additional layers before the output classification, the CNN models will improve the performance of predicting the disease labels for each patient.
Text Classification for Task-based Source Code Related Questions
In this paper, we develop a two-fold deep learning model: Seq2Seq and a binary classifier that takes in the intent (which is in natural language) and code snippets in Python.
Sentiment Analysis in Drug Reviews using Supervised Machine Learning Algorithms
Sentiment Analysis is an important algorithm in Natural Language Processing which is used to detect sentiment within some text.
Fake News Detection with Different Models
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
