Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Abstract Deep Neural Networks have received considerable attention in recent years. As the
complexity of network architecture increases in relation to the task complexity, it becomes …

Neural radiance fields (NeRF) has achieved outstanding performance in modeling 3D
objects and controlled scenes, usually under a single scale. In this work, we focus on multi …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality
retrieval problem, which aims at matching the person-of-interest over visible and infrared …

Highly skewed long-tail item distribution is very common in recommendation systems. It
significantly hurts model performance on tail items. To improve tail-item recommendation …

One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of
candidate architectures. Existing methods either directly use the validation performance or …

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, aka
GATES, to improve the predictor-based neural architecture search. Specifically, different …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Federated learning is a distributed machine learning approach to privacy preservation and
two major technical challenges prevent a wider application of federated learning. One is that …