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Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can …
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical …
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to …
Generating high-quality summaries for chat dialogs often requires large labeled datasets. We propose a method to efficiently use …
Humans possess a remarkable ability to assign novel interpretations to linguistic expressions, enabling them to learn new words and …
Data augmentation is a widely used technique to address the problem of
text classification when there is a limited amount of training …
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
The ability to detect intent in dialogue systems has become increasingly important in modern technology.
These systems often generate a …
Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, …
Handling out-of-distribution (OOD) samples has become a major stake in the real-world deploy- ment of machine learning systems. This …
Embodied agents face significant challenges when tasked with performing actions in diverse environments, particularly in generalizing …