Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their
core is an attention function which models pairwise interactions between the inputs at every …

Text classification is the most fundamental and essential task in natural language processing.
The last decade has seen a surge of research in this area due to the unprecedented …

In digitally controlled dc–dc converters with a single voltage feedback loop, the two quantizers,
namely the analog-to-digital (A/D) converter and the digital pulse-width modulator (DPWM…

Neural models have achieved remarkable success on relation extraction (RE) benchmarks.
However, there is no clear understanding which type of information affects existing RE …

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream
tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is trained on …

Transfer learning aims to provide a framework to utilize previously-acquired knowledge to
solve new but similar problems much more quickly and effectively. In contrast to classical …

Relation classification is an important research arena in the field of natural language
processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the …

Attention mechanisms in neural networks have proved useful for problems in which the
input and output do not have fixed dimension. Often there exist features that are locally …

We study the task of prompting large-scale language models to perform multi-step reasoning.
Existing work shows that when prompted with a chain of thoughts (CoT), sequences of …

The design and characterization of purely organic room‐temperature phosphorescent (RTP)
materials for optoelectronic applications is currently the focus of research in the field of …