Mathematical general relativity reviewed.
problem Challenges in mathematical modeling of general relativity.
method Selected topics reviewed.
result Insights into mathematical models of general relativity.
On-device research index
arXiv research
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.
168,786 papers · 148 categories
Trend · papers per month
48 results for “review generation”
Unsupervised summarization generates novel reviews reflecting consensus opinions.
problem Creating summaries that reflect subjective information in multiple documents.
method Generative model with hierarchical variational autoencoder, pointer-generator mechanism.
result Model produces fluent and coherent summaries reflecting common opinions.
RevGAN generates personalized reviews with given sentiment and style.
problem Creating reviews that are both personalized and of high quality.
method RevGAN combines self-attentive recursive autoencoders, conditional discriminators, and personalized decoders.
result RevGAN outperforms state-of-the-art models in sentence quality, coherence, and personalization.
ScoreGAN uses GANs with IGM to detect bot-generated reviews based on text and scores.
problem Lack of labeled data and bot-generated reviews in fraud review detection.
method ScoreGAN incorporates review text and scores into a GAN framework for data augmentation and detection.
result ScoreGAN outperforms existing methods by 7% and 5% on Yelp and TripAdvisor datasets.
Paper uses pre-trained models and active learning to analyze customer reviews quickly.
problem Automatic review analysis with limited labeled data and time.
method Pre-trained language representation and active learning framework.
result Fully automatic review analysis achieved at a faster pace.
This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has tw…
This paper reviews Douglas curvature in Finsler geometry.
problem Exploring Douglas curvature in Finsler spaces.
method Historical review, characterizations, generalizations, and applications.
result Significance and applications of Douglas curvature in Finsler geometry.
Survey on statistical theories of neural networks, focusing on approximation, training dynamics, and generative models.
problem Understanding the statistical properties and training dynamics of neural networks.
method Review of existing literature on neural networks from three perspectives: approximation, training dynamics, and generative models.
result Theoretical insights into neural network training dynamics and generative models.
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify…
Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them hav…
We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors. This overlap can incentivize strategic reviews to influence the final ranking of one's own papers. In this work, we address this problem through the lens of social choice, and present…
The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such a…
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
Few summaries enable automatic summarization of product reviews.
problem Lack of large labeled datasets for training supervised models in opinion summarization.
method Conditional Transformer model trained to generate summaries given other reviews, fine-tuned to predict summary properties.
result Few summaries (5-10) are sufficient to generate fluent, informative, and sentiment-preserving summaries.
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution lear…
Experiment shows author rankings can improve peer review scores.
problem Improving accuracy in machine learning conference peer review.
method Used Isotonic Mechanism to calibrate review scores using author rankings.
result Calibrated scores outperform raw scores in estimating ground truth review scores.
Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can subm…
This review compares various deep generative models.
problem Training deep neural networks to model data distributions.
method Comprehensive comparison of VAEs, GANs, flows, energy models, and autoregressives.
result Trade-offs and interrelationships among different models.
Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and…
This review connects knot invariants to quiver representations.
problem Relating knot invariants to quiver representations.
method Relates symmetric quivers and their partition functions to quantum invariants of knots.
result Establishes a correspondence between knot invariants and quiver representations.
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100%…
Paper quarantines unreliable Yelp users by detecting review spam.
problem Unreliable and spamming users deceive Yelp's users.
method Used RSD and spam detection techniques on key features.
result More than 80% of Yelp's accounts are unreliable, and highly-rated businesses are often spammed.
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem o…
MPNNs generalize poorly, study reviews current research.
problem Exploring generalization abilities of MPNNs.
method Systematic review of existing literature.
result Limited understanding of MPNNs' generalization abilities.
Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive …
Abstract reviews recent Lagrangian analysis on immersions into higher dimensions.
problem Analyzing Lagrangians on immersions into higher dimensions.
method Reviews recent progress on Lagrangians on immersions with first and second fundamental forms and their derivatives.
result Recent progress in the analysis of Lagrangians on immersions into higher dimensions.
We focus on spectral clustering of unlabeled graphs and review some results on clustering methods which achieve weak or strong consistent identification in data generated by such models. We also present a new algorithm which appears to perform optimally both theoretically using asymptotic theory and empirically.
This review explores ChatGPT in accounting and finance.
problem Understanding the current state of research on ChatGPT in accounting and finance.
method A scoping review of recent publications and working papers.
result Identifies three themes: applications, research tools, and implications.
The abstract reviews models for analyzing count data.
problem Challenges in analyzing count data with standard methods.
method Review of generalized linear models and multinomial models.
result Fundamental connections between multinomial and count models.
The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models. A discriminat…
Proposes RTL model for sentiment classification and key word detection in online reviews.
problem Sentiment classification and key word detection in online reviews for hospitality industry.
method Regularized Text Logistic (RTL) regression model.
result RTL model achieves satisfactory classification performance and identifies key word features.
This study analyzes app reviews to understand students' behavior in the app market.
problem Extracting sentiment from growing app reviews manually is impractical.
method Used machine learning algorithms with TF-IDF for text representation and ensemble learning for evaluation.
result SVM achieved the highest accuracy (93.37%) on tri-gram + TF-IDF scheme.
This paper is a review of the book "Knots" by Alexei Sossinsky. The review includes a short personal history of knot theory at the end of the twentieth century.
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approachescs.AI
This review explores XAI methods and multicollinearity issues.
problem Multicollinearity affects the interpretability of AI models.
method A review of current XAI approaches and their limitations.
result Current XAI methods struggle with multicollinearity.
A review of homomorphic encryption and software tools for encrypted statistical machine learningstat.ML
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent…
Recommender system improves with temporal representations.
problem Improving interpretability and performance in recommender systems.
method Incorporates temporal representations via recurrent point process in continuous time.
result Characterizes effects of perception, interest, and seasonal changes on reviews.
This paper reviews dMTL and methods for selecting auxiliary tasks.
problem Improving model performance for multiple tasks.
method Review of dMTL approaches and methods for selecting auxiliary tasks.
result Methods for selecting auxiliary tasks can improve dMTL performance.
Systematic review of multimodal data challenges and solutions.
problem Challenges in integrating diverse data types for improved diagnostics and personalized care.
method Synthesizing findings from 69 studies on technical obstacles and recent methodological advances.
result Promising solutions like transfer learning, generative models, attention mechanisms, and neural architecture search.
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
Machine learning analyzed peer reviews to find differences in quality by journal impact factor.
problem Determining if higher journal impact factors correlate with more thorough or helpful peer reviews.
method Hand-coded and machine-learned analysis of 10,000 peer review sentences from 1,644 journals.
result Peer reviews in higher impact factor journals are more thorough in discussing methods but less helpful in suggesting solutions and providing examples.
The article reviews how to set stochastic volatility model parameters.
problem Choosing parameters for stochastic volatility models.
method Examines existing literature on various methods.
result Different approaches to setting stochastic volatility parameters.
We enhance autonomous materials research with problem-aware models.
problem Complex decision-making in autonomous materials.
method Bayesian framework, machine learning, physics-based models, operational considerations.
result Improved models reflect problem-specific structure.
Review of instantons in noncommutative gauge theories across 4, 6, and 8 dimensions.
problem Understanding instantons in noncommutative gauge theories.
method Analysis of instantons in various dimensions, focusing on string theory and toric varieties.
result Geometric interpretations and applications of instantons in non-compact toric varieties.
In this paper we review our earlier work on quantum computing and the Nash Equilibrium, in particular, tracing the history of the discovery of new Nash Equilibria and then reviewing the ways in which quantum computing may be expected to generate new classes of Nash equilibria. We then extend this work through a substan…
Neural networks reviewed for option pricing and hedging.
problem Improving option pricing and hedging models using neural networks.
method Comparison of over 100 papers on neural networks for option pricing and hedging.
result Papers compared on various aspects including input features, output variables, and performance measures.
Modular Gaussian curvaturemath.QA
We review the state of the art of our understanding of the conformal geometry of the irrational rotation algebra. This was sparked by a paper by Cohen and Connes. We review the more recent progress made by Connes and the second named author and the work of the authors of this review.
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in th…
Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of diff…