Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

With the ever-growing size of pretrained models (PMs), fine-tuning them has become more
expensive and resource-hungry. As a remedy, low-rank adapters (LoRA) keep the main …

A key aspect of text-to-image personalization methods is the manner in which the target
concept is represented within the generative process. This choice greatly affects the visual …

Federated Learning (FL) has been gaining significant traction across different ML tasks,
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …

Learned representations are a central component in modern ML systems, serving a
multitude of downstream tasks. When training such representations, it is often the case that …

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely
studied for its effectiveness and efficiency. The popular method of low-rank adaptation …

Neural data compression based on nonlinear transform coding has made great progress
over the last few years, mainly due to improvements in prior models, quantization methods …

Natural language is hierarchically structured: smaller units (eg, phrases) are nested within
larger units (eg, clauses). When a larger constituent ends, all of the smaller constituents that …

The input to a machine learning model is a one-dimensional feature vector. However, in
recent learning models, such as convolutional and recurrent neural networks, two-and three …