Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Large Transformer models have been central to recent advances in natural language
processing. The training and inference costs of these models, however, have grown rapidly …

Abstract Machine learning research has advanced in multiple aspects, including model
structures and learning methods. The effort to automate such research, known as AutoML …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

The recent explosion of interest in multimodal applications has resulted in a wide selection
of datasets and methods for representing and integrating information from different …

We investigate a set of techniques for RNN Transducers (RNN-Ts) that were instrumental in
lowering the word error rate on three different tasks (Switchboard 300 hours, conversational …

A large number of neural network models of associative memory have been proposed in the
literature. These include the classical Hopfield networks (HNs), sparse distributed memories …

Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties
has been highlighted as a promising strategy for drug design. Molecular docking program--a …