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Researcher profile

Neil Shah

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

    2016

    Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, …

  2. OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection.

    2017 · arXiv (Cornell University)

    When tasked to find fraudulent social network users, what is a practitioner to do? Traditional classification can lead to poor generalization and high misclassification given few and possibly biased labels. We tackle this problem by …

  3. ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations

    2023 · arXiv (Cornell University)

    We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations. It can train a multi-speaker variant effectively using transcripts from a single speaker. ParrotTTS adapts to a new language in low resource …

  4. Towards Neural Scaling Laws on Graphs

    2024 · arXiv (Cornell University)

    Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural scaling laws on graphs, i.e., how the performance of deep …

  5. How Does Message Passing Improve Collaborative Filtering?

    2024 · arXiv (Cornell University)

    Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural networks, due to its …

  6. Towards Improving NAM-to-Speech Synthesis Intelligibility using Self-Supervised Speech Models

    2024 · arXiv (Cornell University)

    We propose a novel approach to significantly improve the intelligibility in the Non-Audible Murmur (NAM)-to-speech conversion task, leveraging self-supervision and sequence-to-sequence (Seq2Seq) learning techniques. Unlike conventional methods that explicitly record ground-truth speech, our methodology relies …

  7. Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning

    2025

    Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area. To address this …