Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders
(MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can …

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …

Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their
mutual relationships. By using shared resources to simultaneously calculate multiple …

Recent developments in large-scale machine learning suggest that by scaling up data,
model size and training time properly, one might observe that improvements in pre-training …

Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …

Research on the automatic analysis of sonar images has focused on classical, ie non deep
learning based, approaches for a long time. Over the past 15 years, however, the application …

Transfer learning has become a popular method for leveraging pre-trained models in
computer vision. However, without performing computationally expensive fine-tuning, it is …

Learning representations of neural network weights given a model zoo is an emerg-ing and
challenging area with many potential applications from model inspection, to neural …

Deep neural networks such as convolutional neural networks (CNNs) and transformers have
achieved many successes in image classification in recent years. It has been consistently …