Modern foundation model architectures rely on attention mechanisms to
ef...
Multi-task networks rely on effective parameter sharing to achieve robus...
Neural Processes (NPs) are efficient methods for estimating predictive
u...
In many real-world settings, the critical class is rare and a missed
det...
A temporal point process (TPP) is a stochastic process where its realiza...
ML models often operate within the context of a larger system that can a...
Data imbalance, in which a plurality of the data samples come from a sma...
Predicting multiple heterogeneous biological and medical targets is a
ch...
Humans accumulate knowledge in a lifelong fashion. Modern deep neural
ne...
Dynamic Time Warping (DTW) is widely used for temporal data processing.
...
Knowledge distillation is a widely applicable technique for training a
s...
Inspired by the observation that humans are able to process videos
effic...
Robots are increasingly operating in indoor environments designed for an...
Camera relocalization plays a vital role in many robotics and computer v...
Deep residual networks (ResNets) and their variants are widely used in m...
Camera relocalization plays a vital role in many robotics and computer v...
When approaching a novel visual recognition problem in a specialized ima...
Activity analysis in which multiple people interact across a large space...