Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …

In this paper, we consider a highly general image recognition setting wherein, given a
labelled and unlabelled set of images, the task is to categorize all images in the unlabelled …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy
protection; nevertheless, object recognition research typically assumes access to complete …

The objective of this paper is a model that is able to discover, track and segment multiple
moving objects in a video. We make four contributions: First, we introduce an object-centric …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

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 …

We approach self-supervised learning of image representations from a statistical
dependence perspective, proposing Self-Supervised Learning with the Hilbert-Schmidt …

There is a sensory gulf between the Earth that humans inhabit and the digital realms in
which modern AI agents are created. To develop AI agents that can sense, think, and act as …