Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL)
ability, where the model learns to do an unseen task simply by conditioning on a prompt …

Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …

Deep generative models are often used for human motion prediction as they are able to
model multi-modal data distributions and characterize diverse human behavior. While much …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

The determinantal point process (DPP) is an elegant probabilistic model of repulsion with
applications in various machine learning tasks including summarization and search …

Excellent variational approximations to Gaussian process posteriors have been developed
which avoid the $\mathcal {O}\left (N^ 3\right) $ scaling with dataset size $ N $. They reduce …

Contrastive learning has been shown to produce generalizable representations of audio
and visual data by maximizing the lower bound on the mutual information (MI) between …

Video summarization is a challenging problem with great application potential. Whereas
prior approaches, largely unsupervised in nature, focus on sampling useful frames and …

Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

Data representativity is crucial when drawing inference from data through machine learning
models. Scholars have increased focus on unraveling the bias and fairness in models, also …