In multimodal sentiment analysis (MSA), the performance of a model highly depends on the
quality of synthesized embeddings. These embeddings are generated from the upstream …

Complex models in physics, biology, economics, and engineering are often ill-determined or
sloppy: their multiple parameters can vary over wide ranges without significant changes in …

Dual-function radar communication (DFRC) systems implement both sensing and
communication using the same hardware. Such schemes are often more efficient in terms of …

Registration of range sensor measurements is an important task in mobile robotics and has
received a lot of attention. Several iterative optimization schemes have been proposed in …

Uncertainty propagation of dynamical systems is a common need across many domains and
disciplines. In nonlinear settings, the extended Kalman filter is the de facto standard …

Mixture distributions arise in many parametric and non-parametric settings—for example, in
Gaussian mixture models and in non-parametric estimation. It is often necessary to compute …

Since convolutional neural networks (CNNs) achieved top performance on the ImageNet
task in 2012, deep learning has become the preferred approach to addressing computer …

This paper studies defense mechanisms against model inversion (MI) attacks--a type of
privacy attacks aimed at inferring information about the training data distribution given the …

Variational methods are widely used for approximate posterior inference. However, their use
is typically limited to families of distributions that enjoy particular conjugacy properties. To …

Interpreting visual scenes typically requires us to accumulate information from multiple
locations in a scene. Using a novel gaze-contingent paradigm in a visual categorization …