Large Language Models (LLMs) recently demonstrated extraordinary capability in various
natural language processing (NLP) tasks including language translation, text generation …

The explosive growth of online education environments is generating a massive volume of
data, specially in text format from forums, chats, social networks, assessments, essays …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Pretrained language models (PTLMs) are typically learned over a large, static corpus and
further fine-tuned for various downstream tasks. However, when deployed in the real world …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Moving from limited-domain natural language generation (NLG) to open domain is difficult
because the number of semantic input combinations grows exponentially with the number of …

In this paper, a new training paradigm is proposed for deep reinforcement learning using
self-paced prioritized curriculum learning with coverage penalty. The proposed deep …

The wide-angle lens shows appealing applications in VR technologies, but it introduces
severe radial distortion into its captured image. To recover the realistic scene, previous …

High-quality whole slide scanners used for animal and human pathology scanning are
expensive and can produce massive datasets, which limits the access to and adoption of …