---
# System prepended metadata

title: Papers RNN&Contrastive

---

### Continuous Encoder

META TEMPORAL POINT PROCESSES
https://arxiv.org/pdf/2301.12023.pdf

code and etc: https://openreview.net/forum?id=QZfdDpTX1uM


Simplified State Space Layers for Sequence Modeling
https://arxiv.org/abs/2208.04933

SIMPLIFIED STATE SPACE LAYERS FOR SEQUENCE
MODELING
https://arxiv.org/pdf/2208.04933.pdf

Modeling Irregular Time Series with Continuous Recurrent Units
https://proceedings.mlr.press/v162/schirmer22a/schirmer22a.pdf

Closed-form Continuous-time Neural Networks
https://arxiv.org/pdf/2106.13898.pdf

Semi-supervised sequence classification through change point detection
https://arxiv.org/pdf/2009.11829.pdf

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://arxiv.org/pdf/2106.13898.pdf

Continuous-time convolutions model of event sequences
https://arxiv.org/pdf/2302.06247.pdf

Diagonal State Spaces are as Effective as Structured State Spaces
https://proceedings.neurips.cc/paper_files/paper/2022/file/9156b0f6dfa9bbd18c79cc459ef5d61c-Paper-Conference.pdf

Time-series Generative Adversarial Networks
https://proceedings.neurips.cc/paper_files/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf

Neural Continuous-Discrete State Space Models
for Irregularly-Sampled Time Series
https://arxiv.org/pdf/2301.11308.pdf


## Modern RNN

RWKV: Reinventing RNNs for the Transformer Era
https://arxiv.org/abs/2305.13048

RRWKV: Capturing Long-range Dependencies in RWKV
https://arxiv.org/pdf/2306.05176.pdf

Retentive Network: A Successor to Transformer
for Large Language Models
https://arxiv.org/pdf/2307.08621.pdf



## Contrastive NLP

https://github.com/ryanzhumich/Contrastive-Learning-NLP-Papers

### Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

## Contrasitve and self supervises methods:
- A Survey of Self-Supervised Learning from Multiple Perspectives: Algorithms, Theory, Applications and Future Trends https://arxiv.org/pdf/2301.05712.pdf

### Denoising Diffusion Autoencoders are Unified Self-supervised Learners
https://arxiv.org/abs/2303.09769

## Event sequence representations

### TS2Vec: Towards Universal Representation of Time Series
### Time Series Contrastive Learning with Information-Aware Augmentations
### Time-Series Representation Learning via Temporal and Contextual Contrasting
### Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC)
### A Survey on Time-Series Pre-Trained Models

### Coles: contrastive learning for event sequences with self-supervision
### DuETT: Dual Event Time Transformer for Electronic Health Record

### Contrastive self-supervised sequential recommendation with robust augmentation
## KSOM
### Improving contrastive learning with model augmentation
### Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation


### Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation


### Contrastvae: Contrastive variational autoencoder for sequential recommendation

### Bootstrapping user and item representations for one-class collaborative filtering

### CL4CTR: A Contrastive Learning Framework for CTR Prediction

### Siamese Masked Autoencoders

### Byol for audio: Self-supervised learning for general-purpose audio representation

### Byol-s: Learning self-supervised speech representations by bootstrapping

### Audio Barlow Twins: Self-Supervised Audio Representation Learning

### Non-contrastive approaches to similarity learning: positive examples are all you need



Topological Neural Discrete Representation Learning\a la Kohonen

Som-vae: Interpretable discrete representation learning on time series



Time2Vec: Learning a Vector Representation of Time 2019 c: 222

## TPP


Hierarchical Contrastive Learning for Temporal Point Processes

Recurrent Marked Temporal Point Processes: Embedding Event History to Vector 2016 c: 629
Multi-Time Attention Networks for Irregularly Sampled Time Series 2021 c: 78
https://github.com/reml-lab/mTAN


Modeling Irregular Time Series with Continuous Recurrent Units 2021 c: 19
https://arxiv.org/pdf/2111.11344.pdf#page=2&zoom=100,96,926


Simplified State Space Layers for Sequence Modeling 2022 с: 29

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation 2021 c: 105

INITIATOR: Noise-contrastive Estimation for Marked Temporal 
Point Process2018 c: 33

Continuous-time convolutions model of event sequences
POINT PROCESS FLOWS  2019 c: 11
https://arxiv.org/pdf/1910.08281.pdf

Neural Spatio-Temporal Point Processes 2020 c: 61
https://arxiv.org/pdf/2011.04583.pdf

Transformer Hawkes Process

MULTI-TIME ATTENTION NETWORKS FOR IRREGULARLY SAMPLED TIME SERIES

Latent ODEs for Irregularly-Sampled Time Series

META TEMPORAL POINT PROCESSES

TRANSFORMER EMBEDDINGS OF IRREGULARLY SPACED EVENTS AND THEIR PARTICIPANTS

