Search Results for author:
Found 9 papers, 7 papers with code
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields.
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings.
RecipeSnap -- a lightweight image-to-recipe model
Recipes from Recipe1M dataset and corresponding recipe embeddings are collected as a recipe library, which are used for image encoder training and image query later.
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results.
Duluth at SemEval-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines
We use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines.
Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning
Using the 4FGL catalog, a sample of 3137 Fermi blazars with 23 parameters is systematically selected.
High Energy Astrophysical Phenomena
Neural Document Expansion with User Feedback
This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models.
Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph
While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction.
