Education
MultiModN--Multimodal, Multi-Task, Interpretable Modular Networks
Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned semantic meaning across inputs of drastically varying sizes (i.e.
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
AI/ML problems, and in each such case ฯ is assumed to be known .
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense Kalpesh Krishna Yixiao Song Marzena Karpinska John Wieting Mohit Iyyer
API, looking for sequences that match the candidate text within a certain threshold.
Canonical normalizing flows for manifold learning
The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations.
Hyper-HMM: aligning human brains and semantic features in a common latent event space
Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.
Making Scalable Meta Learning Practical
Meta learning aims to learn the inductive biases ( e .
