With the resurgence of interest in neural networks, representation learning has re-emerged
as a central focus in artificial intelligence. Representation learning refers to the discovery of …

What makes images similar? To measure the similarity between images, they are typically
embedded in a feature-vector space, in which their distance preserve the relative …

We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in
operationalizing their approach is the difficulty in eliciting a human specification of a …

Visual question answering is the task of returning the answer to a question about an image.
A challenge is that different people often provide different answers to the same visual …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, establish the
correlation between texts and images, achieving remarkable success on various …

The knowledge of a well-trained deep neural network (aka the “teacher”) is valuable for
learning similar tasks. Knowledge distillation extracts knowledge from the teacher and …

Meta-learning has emerged as an efficient approach for constructing target models based
on support sets. For example, the meta-learned embeddings enable the construction of …

The discriminative knowledge from a high-capacity deep neural network (aka the" teacher")
could be distilled to facilitate the learning efficacy of a shallow counterpart (aka the" …

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract
items, using only answers to triplet comparisons of the form" Is item i closer to item j or item …

Visual question answering systems empower users to ask any question about any image
and receive a valid answer. However, existing systems do not yet account for the fact that a …