Results 101 to 110 of about 8,049,290 (285)

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora

Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development.
Plaza-del-Arco, Flor Miriam, Klinger, Roman, Martín-Valdivia, María-Teresa   +2 more
core   +1 more source

Automated Assessment of Inferences Using Pre-Trained Language Models

Applied Sciences
Inference plays a key role in reading comprehension. However, assessing inference in reading is a complex process that relies on the judgment of trained experts. In this study, we explore objective and automated methods for assessing inference in readers’
Yongseok Yoo
doaj   +1 more source

On Reference (In-)Determinacy in Natural Language Inference

Findings of the Association for Computational Linguistics: NAACL 2025
We revisit the reference determinacy (RD) assumption in the task of natural language inference (NLI), i.e., the premise and hypothesis are assumed to refer to the same context when human raters annotate a label. While RD is a practical assumption for constructing a new NLI dataset, we observe that current NLI models, which are typically trained solely ...
Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, Hagai Taitelbaum, Tal Schuster, Senaka Buthpitiya, Dan Roth 0001   +6 more
openaire   +3 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Instance Weight-Based Training: Paradigm Shift in Mini-Batch Training for Multiclass Classification in Natural Language Processing

IEEE Access
This paper presents a novel perspective on training methods that diverges from traditional fine-tuning approaches for deep learning-based natural language processing models by considering the weight of each instance.
Seung-Hyeok Park, Jeongil Park, Byung-Won On, Ingyu Lee   +3 more
doaj   +1 more source

Sequence-to-Sequence Text Generation with Discrete Diffusion Models [PDF]

Jisuanji kexue yu tansuo
Diffusion language models are currently the most promising language models among non-autoregressive models, and are expected to replace autoregressive language models, which suffer from slow inference speed, to achieve efficient and quality-preserving ...
JIANG Hang, CAI Guoyong, LI Sihui
doaj   +1 more source

On the Effective Use of Pretraining for Natural Language Inference

CoRR, 2017
Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-to-sequence models for natural language inference (NLI ...
Ignacio Cases, Minh-Thang Luong, Christopher Potts   +2 more
openaire   +3 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source