Temporal Sequences

35 papers with code • 1 benchmarks • 3 datasets

This task asks models to answer questions about which times certain events could have occurred.

Source: BIG-bench

Image source: BIG-bench

Most implemented papers

Functional Map of the World

facebookresearch/DomainBed CVPR 2018

We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views.

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

nnzhan/Graph-WaveNet 31 May 2019

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

fferroni/PhasedLSTM-Keras NeurIPS 2016

In this work, we introduce the Phased LSTM model, which extends the LSTM unit by adding a new time gate.

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

soroushmehr/sampleRNN_ICLR2017 22 Dec 2016

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

js3611/Deep-MRI-Reconstruction 5 Dec 2017

In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.

shapeDTW: shape Dynamic Time Warping

jiapingz/shapeDTW 6 Jun 2016

Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments.

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

psu1/DeepRNN 12 May 2017

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.

State-Frequency Memory Recurrent Neural Networks

hhkunming/State-Frequency-Memory-Recurrent-Neural-Networks ICML 2017

Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community.

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

INK-USC/RE-Net 11 Apr 2019

The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.

Learning Character-Agnostic Motion for Motion Retargeting in 2D

ChrisWu1997/2D-Motion-Retargeting 5 May 2019

In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.