A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
Estimates citation impact to recommend best publication venue.
Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions and journals. While citations are raw data for constructing impact measures, there exist biases and potential issues if factors affecting citation patterns are not properly account…
Visuals in scientific papers are used to express complex ideas; this study uses them to identify knowledge domains.
Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional inten…
Public data boosts MICCAI papers' citations, but sharing remains low.
Develops a deep learning architecture for rich-item recommendations.
SentiCite analyzes citations for sentiment and nature, improving on existing methods.
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to lear…
A multi-task model tackles citation purpose classification with limited data.
Synthetic reference strings are as effective as real ones for training citation parsing models.
Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure …
A combined model integrates latent factor and logistic regression for citation network analysis.
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advan…
Corrects errors in Hans' pseudocovering spaces paper.
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nod…
Paper develops an attention mechanism for long-term scientific impact prediction.
Study reveals bias in machine learning conference reviews.
Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still d…
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches …
This paper asks, "Do classics exist in megaproject management?" We identify three types of classic texts: conventional, Kuhnian, and citation classics. We find that the answer to our question depends on the definition of "classic" employed. First, "citation classics" do exist in megaproject management, and they perform…
Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic li…
Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-…
We correct a mistake on the citation of JSJ theory in \cite{Ni}. Some arguments in \cite{Ni} are also slightly modified accordingly.
Novel graph network learns hierarchical network structure.
Improved AI patent classifier measures U.S. and China's AI patenting.
Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on…
System identifies high impact research from academic papers.
We present a hybrid method for latent information discovery on the data sets containing both text content and connection structure based on constrained low rank approximation. The new method jointly optimizes the Nonnegative Matrix Factorization (NMF) objective function for text clustering and the Symmetric NMF (SymNMF…
A novel model-selection method for dynamic networks using synthetic data.
Paper introduces a core-periphery model for identifying informative network structures.
Proposes dynamic graph and node feature learning in GCNNs for better adaptability.
RoGAT enhances GAT robustness against adversarial attacks.
DREAM model improves computational efficiency for non-linear effects in relational event models.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
In sustained growth with random dynamics stationary distributions can exist without detailed balance. This suggests thermodynamical behavior in fast growing complex systems. In order to model such phenomena we apply both a discrete and a continuous master equation. The derivation of elementary rates from known stationa…
Proposes a new neural head for asymmetric representation learning.
The paper models network formation using mixed logit models.
CAFL breaks feedback loops in recommender systems using causal inference.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existi…
The paper aims to define a benchmark for deep learning recommendation models.
Interprets feature interactions in ad-click prediction models.
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsa…
Survey on using knowledge graphs for better recommender systems.
Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection by candidates using machine learning. Additionally, our recomme…
Data poisoning attacks can manipulate recommender systems to recommend target items.
Proposes CF-SFL to improve sparse data recommendation.