前段时间 没事刷paper， 把时间序列，时空序列，异常检测的paper稍微聚集了一下，当然可能存在不对称的paper~ 希望大家别太在意~

整理的比较松散，大部分正在reading

Self-supervised Co-Training for Video Representation Learning

Tengda Han (University of Oxford) · Weidi Xie (University of Oxford) · Andrew Zisserman (DeepMind & University of Oxford)

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

Vincent LE GUEN (CNAM, Paris, France) · Nicolas THOME (Cnam (Conservatoire national des arts et métiers))

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

Davis Rempe (Stanford University) · Tolga Birdal (Technical University of Munich) · Yongheng Zhao (University of Padova) · Zan Gojcic (ETH Zürich) · Srinath Sridhar (Stanford University) · Leonidas J Guibas (stanford.edu)

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso

Jerome-Alexis Chevalier (Inria Saclay Île-de-France) · Joseph Salmon (Université de Montpellier) · Alexandre Gramfort (INRIA) · Bertrand Thirion (INRIA)

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks

Wenrui Zhang (University of California, Santa Barbara) · Peng Li (University of California, Santa Barbara)

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

Michael Janner (UC Berkeley) · Igor Mordatch (Google) · Sergey Levine (UC Berkeley)

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Yonggan Fu (Rice University) · Haoran You (Rice University) · Yang Zhao (Rice University) · Yue Wang (Rice University) · Chaojian Li (Rice University) · Kailash Gopalakrishnan (IBM Research) · Zhangyang Wang (University of Texas at Austin) · Yingyan Lin (Rice University)

Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network

Lifeng Shen (The Hong Kong University of Science and Technology) · Zhuocong Li (Tencent) · James Kwok (Hong Kong University of Science and Technology)

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

Vincent LE GUEN (CNAM, Paris, France) · Nicolas THOME (Cnam (Conservatoire national des arts et métiers))

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

Davis Rempe (Stanford University) · Tolga Birdal (Technical University of Munich) · Yongheng Zhao (University of Padova) · Zan Gojcic (ETH Zürich) · Srinath Sridhar (Stanford University) · Leonidas J Guibas (stanford.edu)

ARMA Nets: Expanding Receptive Field for Dense Prediction

Jiahao Su (University of Maryland) · Shiqi Wang (Nanjing University ) · Furong Huang (University of Maryland)

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning

Jiachen Li (University of California, Berkeley) · Fan Yang (University of California, Berkeley) · Masayoshi Tomizuka (University of California, Berkeley) · Chiho Choi (Honda Research Institute US)

Benchmarking Deep Learning Interpretability in Time Series Predictions

Aya Abdelsalam Ismail (University of Maryland) · Mohamed Gunady (University of Maryland) · Hector Corrada Bravo (University of Maryland) · Soheil Feizi (University of Maryland)

Multi-agent Trajectory Prediction with Fuzzy Query Attention

Nitin Kamra (University of Southern California) · Hao Zhu (Peking University) · Dweep Kumarbhai Trivedi (University of Southern California) · Ming Zhang (Peking University) · Yan Liu (University of Southern California)

Deep reconstruction of strange attractors from time series

William Gilpin (Harvard University)

Adversarial Sparse Transformer for Time Series Forecasting

Sifan Wu (Tsinghua University) · Xi Xiao (Tsinghua University) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Ying Wei (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

Learning Long-Term Dependencies in Irregularly-Sampled Time Series

Mathias Lechner (IST Austria) · Ramin Hasani (MIT)

High-recall causal discovery for autocorrelated time series with latent confounders

Andreas Gerhardus (German Aerospace Center (DLR)) · Jakob Runge (Institute of Data Science, German Aerospace Center (DLR))

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

Richard Kurle (Volkswagen Group) · Syama Sundar Rangapuram (Amazon Research) · Emmanuel de Bézenac (Sorbonne Université) · Stephan Günnemann (Technical University of Munich) · Jan Gasthaus (Amazon.com)

Normalizing Kalman Filters for Multivariate Time Series Analysis

Emmanuel de Bézenac (Sorbonne Université) · Syama Sundar Rangapuram (Amazon Research) · Konstantinos Benidis (Amazon Research) · Michael Bohlke-Schneider (Amazon) · Lorenzo Stella (Amazon Research) · Hilaf Hasson (Amazon Research) · Richard Kurle (Volkswagen Group) · Tim Januschowski (Aamazon Research) · Patrick Gallinari (Sorbonne University & Criteo AI Lab, Paris)a

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