Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …

Cognitive science applies diverse tools and perspectives to study human language.
Recently, an exciting body of work has examined linguistic phenomena through the lens of …

Vision-language representation learning largely benefits from image-text alignment through
contrastive losses (eg, InfoNCE loss). The success of this alignment strategy is attributed to …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The output of text-to-image synthesis systems should be coherent, clear, photo-realistic
scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal …

Contrastive learning between multiple views of the data has recently achieved state of the
art performance in the field of self-supervised representation learning. Despite its success …

In this paper, we propose a simple unsupervised graph representation learning method to
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …

We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …

Applications such as weather forecasting and personalized medicine demand models that
output calibrated probability estimates---those representative of the true likelihood of a …

Despite their great success, there is still no comprehensive theoretical understanding of
learning with Deep Neural Networks (DNNs) or their inner organization. Previous work …