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APL Machine Learning is a peer-reviewed journal published by AIP Publishing, covering Machine Learning in Materials Science, Advanced Memory and Neural Computing, Neural Networks and Reservoir Computing. Over its lifetime it has published 259 papers, which have received 1,633 citations.
| Publisher | AIP Publishing |
|---|---|
| Reference style | Numbered (AIP) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, APL Machine Learning 12, 45 (2023).
Formats any DOI in APL Machine Learning style. No sign-up. |
| Publishes research in | Machine Learning in Materials Science Advanced Memory and Neural Computing Neural Networks and Reservoir Computing Ferroelectric and Negative Capacitance Devices Model Reduction and Neural Networks |
| ISSN | 2770-9019 |
| Citation impact (2-yr) | 4.1 |
| h-index | 18 |
| i10-index | 42 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | The University of Tokyo |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or suppress the memory bottleneck, which is the major concern for energy efficiency and latency in modern digital computing. While the IMC concept is simple and promising, the details of its implementation cover a broad range of problems and solutions, including various…
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs’ operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current…
Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can…
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In…
We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and…
