Describing statistical dependencies is foundational to empirical scientific research. For
uncovering intricate and possibly nonlinear dependencies between a single target variable …

In order to perform multimodal fusion of heterogeneous signals, we need to understand their
interactions: how each modality individually provides information useful for a task and how …

Spurious patterns refer to a mathematical association between two or more variables in a
dataset that are not causally related. However, this notion of spuriousness, which is usually …

In modern deep neural networks, the learning dynamics of the individual neurons is often
obscure, as the networks are trained via global optimization. Conversely, biological systems …

Advances in multimodal models have greatly improved how interactions relevant to various
tasks are modeled. Today's multimodal models mainly focus on the correspondence …

Multi-modal learning has achieved remarkable success by integrating information from
various modalities, achieving superior performance in tasks like recognition and retrieval …

Humans perceive the world through multisensory integration, blending the information of
different modalities to adapt their behavior. Contrastive learning offers an appealing solution …

Despite significant advancements in computer vision, understanding complex scenes,
particularly those involving humor, remains a substantial challenge. This paper introduces …

Knowledge distillation provides an effective method for deploying complex machine learning
models in resource-constrained environments. It typically involves training a smaller student …

Multimodal machine learning, which studies the information and interactions across various
input modalities, has made significant advancements in understanding the relationship …