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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.

168,742 papers · 148 categories

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4 results for “Snapchat”

Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchatcs.LG

While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time w…

2019-06-02abs ↗pdf ↗

Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lensescs.LG

GANs can bias synthetic data, affecting minority and female faces.

problem GANs can amplify biases in synthetic data augmentation.

method Examine GANs on face-shots with gender and skin tone biases.

result GANs generate biased synthetic data, skewing minority modes and features.

Generative Adversarial Networks Bias Facial Data Augmentation Snapchat Machine Learning

2020-01-26abs ↗pdf ↗

Themes Informed Audio-visual Correspondence Learningcs.AI

Paper proposes new principles and framework for AVC learning from user-generated videos.

problem Challenges in learning audio-visual correspondence from short-term user-generated videos.

method Introduced new principles and a framework to facilitate AVC learning from videos' themes.

result Proposed approach outperformed baseline by 23.15% on KWAI-AD-AudVis corpus.

audio-visual correspondence user-generated videos short-term videos machine learning multimodal learning

2020-09-14abs ↗pdf ↗

Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Appscs.SI

FATE predicts user engagement on social apps with explainable explanations.

problem Accurate user engagement prediction for social apps with explainability.

method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.

result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.

user engagement social apps explainable AI friendships actions

2020-06-10abs ↗pdf ↗