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Found 17 papers, 3 papers with code
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization.
Representation Reliability and Its Impact on Downstream Tasks
Self-supervised pre-trained models extract general-purpose representations from data, and quantifying how reliable they are is crucial because many downstream models use these representations as input for their own tasks.
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making.
Assumption-Free Survival Analysis Under Local Smoothness Prior
Survival analysis appears in various fields such as medicine, economics, engineering, and business.
Global-Local Item Embedding for Temporal Set Prediction
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e. g., personalized purchase prediction of shopping baskets.
One4all User Representation for Recommender Systems in E-commerce
General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields.
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
Probabilistic time-series models become popular in the forecasting field as they help to make optimal decisions under uncertainty.
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
div2vec: Diversity-Emphasized Node Embedding
Recently, the interest of graph representation learning has been rapidly increasing in recommender systems.
Multi-Manifold Learning for Large-scale Targeted Advertising System
The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space.
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
The hop sampling randomly selects the number of propagation steps rather than fixing it, and by doing so, it encourages the model to learn meaningful node representation for all intermediate propagation layers and to experience a variety of plausible graphs that are not in the training set.
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly.
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well.
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e. g., video.
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics
To resolve the challenge, this paper proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics.
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e. g., video.
Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization
This paper proposes a Bayesian framework for localization of multiple sources in the event of accidental hazardous contaminant release.
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