- Fluid simulation with dynamic Boltzmann machine in batch manner, Kun Zhao, Takayuki Osogami and Rudy Raymond.
- Learning theory and algorithms for shapelets and other local features, Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai and Akiko Takeda
- Elastic Motif Segmentation and Alignment of Time Series for Encoding and Classification, Tao-Yi Lee, Yuh-Jye Lee, Hsing-Kuo Pao, You-Hua Lin and Yi-Ren Yeh
- Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks, Víctor Campos, Brendan Jou, Xavier Giró-I-Nieto, Jordi Torres and Shih-Fu Chang
- Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification, Achintya Kr. Sarkar and Zheng-Hua Tan
- DP Mixture of Warped Correlated GPs for Individualized Time Series Prediction, Yun Jie Serene Yeo, Kian Ming A. Chai, Weiping Priscilla Fan, Si Hui Maureen Lee, Junxian Ong, Poh Ling Tan, Yu Li Lydia Law and Kok-Yong Seng
- Multi-Scale Change Point Detection in Multivariate Time Series, Zahra Ebrahimzadeh and Samantha Kleinberg
- Bayesian Time Series Forecasting with Change Point and Anomaly Detection, Anderson Zhang, Miao Lu, Deguang Kong and Jimmy Yang
- Discovering order in unordered datasets: Generative Markov Networks, Yao-Hung Hubert Tsai, Han Zhao, Nebojsa Jojic and Ruslan Salakhutdinov
- Towards Desynchronization Detection in Biosignals, Akara Supratak, Steffen Schneider, Hao Dong, Ling Li and Yike Guo
- Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, Yaguang Li, Rose Yu, Cyrus Shahabi and Yan Liu
- Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions, Rudy Raymond, Takayuki Osogami and Sakyasingha Dasgupta
- Structured Inference for Recurrent Hidden Semi-markov Model, Hao Liu, Haoli Bai, Lirong He and Zenglin Xu
- Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data, Petar Veličković, Laurynas Karazija, Nicholas Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Lio, Angela Chieh, Otmane Bellahsen and Matthieu Vegreville
- Sequence modeling using a memory controller extension for LSTM, Itamar Ben-Ari and Ravid Shwartz-Ziv
- Bayesian delay embeddings for dynamical systems, Neil Dhir, Adam Kosiorek and Ingmar Posner
- Time Series Forecasting = Matrix Estimation, Anish Agarwal, Muhammad Amjad, Devavrat Shah and Dennis Shen
- Long-term Forecasting using Tensor-Train RNNs, Rose Yu, Stephan Zheng, Anima Anandkumar and Yisong Yue
- Temporal Clustering in time-varying Networks with Time Series Analysis, Kun Tu, Bruno Ribeiro, Ananthram Swami and Don Towsley
- Time Series Classification with Causal Compression, Aleksander Wieczorek and Volker Roth
- Variational inference for latent nonlinear dynamics, Daniel Hernandez, Liam Paninski and John Cunningham
- Trend Filtering in Network Time Series with Applications to Traffic Incident Detection, Pranamesh Chakraborty, Chinmay Hegde and Anuj Sharma
- MiDGaP: Mixture Density Gaussian Processes, Jaleh Zand and Stephen Roberts
- Vector-Valued Spectral Analysis of Space-Time Data, Dimitrios Giannakis, Joanna Slawinska, Abbas Ourmazd and Zhizhen Zhao
- An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series, Alex Tank, Emily Fox and Ali Shojaie
- An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery, Alex Tank, Ian Covert, Nick Foti, Ali Shojaie and Emily Fox
- A Multi-Horizon Quantile Recurrent Forecaster, Ruofeng Wen, Kari Torkkola and Balakrishnan Narayanaswamy
- Convolutional Sequence Modeling Revisited, Shaojie Bai, J. Zico Kolter and Vladlen Koltun
- Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals, John Alberg and Zachary Lipton
- Non-Stationary Streaming PCA, Apurv Shukla, Se-Young Yun and Daniel Bienstock
- Scalable Joint Models for Reliable Event Prediction, Hossein Soleimani, James Hensman and Suchi Saria
- Closed-form Inference and Prediction in Gaussian Process State-Space Models, Alessandro Davide Ialongo, Mark van der Wilk and Carl Edward Rasmussen