Representation Learning
1289 papers with code • 2 benchmarks • 1 datasets
Representation learning is concerned with training machine learning algorithms to learn useful representations, e.g. those that are interpretable, have latent features, or can be used for transfer learning.
( Image credit: Visualizing and Understanding Convolutional Networks )
Benchmarks
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Sports10
Subtasks
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Word Embeddings
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Graph Embedding
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Graph Representation Learning
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Network Embedding
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Network Embedding
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Unsupervised Representation Learning
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Sentence Embeddings
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Knowledge Graph Embedding
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Sentence Embedding
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Knowledge Graph Embeddings
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Learning Word Embeddings
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Document Embedding
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Multilingual Word Embeddings
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Learning Semantic Representations
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Learning Network Representations
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Sentence Embeddings For Biomedical Texts
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Learning Representation Of Multi-View Data
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Learning Representation On Graph
Greatest papers with code
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
tensorflow/models
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ICLR 2019
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
Semi-Supervised Sequence Modeling with Cross-View Training
tensorflow/models
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EMNLP 2018
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
Ranked #1 on
CCG Supertagging
on CCGbank
Meta-Learning Update Rules for Unsupervised Representation Learning
tensorflow/models
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ICLR 2019
Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #5 on
Image Clustering
on Tiny-ImageNet
Unsupervised Cross-lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.
Unsupervised Cross-lingual Representation Learning at Scale
huggingface/transformers
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ACL 2020
We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale.
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
google-research/google-research
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ICLR 2021
Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states.
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
google-research/google-research
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•
In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT.
Supervised Contrastive Learning
google-research/google-research
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NeurIPS 2020
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
Ranked #234 on
Image Classification
on ImageNet
On Mutual Information Maximization for Representation Learning
google-research/google-research
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ICLR 2020
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data.
Representation Learning
Self-Supervised Image Classification