Representation Learning

Self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

A generic way of representing and interpolating labels, which allows straightforward extension of any kind of mixup to deep metric learning for a large class of loss functions.

NeuroTactic is a model for theorem proving which leverages graph neural networks to represent the theorem and premises, and applies graph contrastive learning for pre-training. Specifically, premise selection is designed as a pretext task for the graph contrastive learning approach. The learned representations are then used for the downstream task, tactic prediction

Poincaré Embeddings learn hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an $n$-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows for learning of parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. Embeddings are learnt based on Riemannian optimization.

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A method to disentangle pose from other factors in a scene.

RESCAL model trained with a relation prediction objective on top of the 1vsAll loss

SMITH, or Siamese Multi-depth Transformer-based Hierarchical Encoder, is a Transformer-based model for document representation learning and matching. It contains several design choices to adapt self-attention models for long text inputs. For the model pre-training, a masked sentence block language modeling task is used in addition to the original masked word language model task used in BERT, to capture sentence block relations within a document. Given a sequence of sentence block representation, the document level Transformers learn the contextual representation for each sentence block and the final document representation.

TABBIE is a pretraining objective (corrupt cell detection) that learns exclusively from tabular data. Unlike other approaches, TABBIE provides embeddings of all table substructures (cells, rows, and columns). TABBIE can be seen as a table embedding model trained to detect corrupted cells, inspired by the ELECTRA objective function.

TuckER model trained with a relation prediction objective on top of the 1vsAll loss

VC R-CNN is an unsupervised feature representation learning method, which uses Region-based Convolutional Neural Network (R-CNN) as the visual backbone, and the causal intervention as the training objective. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn "sense-making" knowledge like chair can be sat -- while not just "common" co-occurrences such as the chair is likely to exist if table is observed.