International Conference on Learning Representations

Academic conference in machine learning

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Curriculum learning Rule-based learning Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
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International Conference on Learning Representations
Abbreviation	ICLR
Discipline	Machine learning, artificial intelligence, feature learning
Publication details
History	2013–present
Frequency	Annual
Open access	yes (on openreview.net)
Website	https://iclr.cc/

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year. The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun^[1]). In 2019, there were 1591 paper submissions, of which 500 accepted with poster presentations (31%) and 24 with oral presentations (1.5%).^[2] In 2021, there were 2997 paper submissions, of which 860 were accepted (29%).^[3]

Locations

ICLR 2024, Vienna, Austria
ICLR 2023, Kigali, Rwanda
ICLR 2022 (virtual conference)
ICLR 2021, Vienna, Austria (virtual conference)
ICLR 2020, Addis Ababa, Ethiopia (virtual conference)^[4]^[5]
ICLR 2019, New Orleans, Louisiana, United States
ICLR 2018, Vancouver, Canada
ICLR 2017, Toulon, France
ICLR 2016, San Juan, Puerto Rico, United States
ICLR 2015, San Diego, California, United States
ICLR 2014, Banff National Park, Canada
ICLR 2013, Scottsdale, Arizona, United States

References

^ "Proposal for A New Publishing Model in Computer Science". yann.lecun.com.
^ "ICLR 2019 Conference". openreview.net.
^ "ICLR 2021 Conference". openreview.net.
^ "Major AI conference is moving to Africa in 2020 due to visa issues". 19 November 2018.
^ "Major AI conference is moving to Africa in 2020 due to visa issues". VentureBeat. 2018-11-19. Retrieved 2020-10-07.

External links

Official website

Differentiable computing

General

Concepts

Gradient descent
- SGD
Clustering
Regression
- Overfitting
Hallucination
Adversary
Attention
Convolution
Loss functions
Backpropagation
Batchnorm
Activation
- Softmax
- Sigmoid
- Rectifier
Regularization
Datasets
- Augmentation
Diffusion
Autoregression

Applications

Hardware

Software libraries

TensorFlow
PyTorch
Keras
Theano
JAX
Flux.jl
MindSpore

Implementations

Audio–visual	AlexNet WaveNet Human image synthesis HWR OCR Speech synthesis Speech recognition Facial recognition AlphaFold Text-to-image models DALL-E Midjourney Stable Diffusion Text-to-video models Sora Whisper
Verbal	Word2vec Seq2seq BERT Gemini LaMDA Bard NMT Project Debater IBM Watson IBM Watsonx Granite GPT-1 GPT-2 GPT-3 GPT-4 ChatGPT GPT-J Chinchilla AI PaLM BLOOM LLaMA PanGu-Σ
Decisional	AlphaGo AlphaZero Q-learning SARSA OpenAI Five Self-driving car MuZero Action selection Auto-GPT Robot control