ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring
Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very …
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ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring
DeepABM: Scalable and Efficient Agent-Based Simulations via Graph Convolutional Frameworks - a Case Study for COVID-19 Spread and Interventions
We introduce DeepABM, a computational framework for agent-based modeling that leverages geometric message passing for simulating action …
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LT-GAN: Self-Supervised GAN with Latent Transformation Detection
Generative Adversarial Networks (GANs) coupled with self-supervised tasks have shown promising results in unconditional and …
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Medical Dead-ends and Learning to Identify High-Risk States and Treatments
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal …
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Robustness of Markov perfect equilibrium to model approximations in general-sum dynamic games
Dynamic games (also called stochastic games or Markov games) are an important class of games for modeling multi-agent interactions. In …
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Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring
Automatic Speech Scoring (ASS) is the computer-assisted evaluation of a candidate’s speaking proficiency in a language. ASS …
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TAN-NTM: Topic Attention Networks for Neural Topic Modeling
Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, …
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What Ails One-Shot Image Segmentation: A Data Perspective
One-shot image segmentation (OSS) methods enable semantic labeling of image pixels without supervised training using an extensive …
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ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors
Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has …
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 An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing …
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