Search Results - Engineering::Computer science AND engineering::Computing methodologies::Artificial intelligence

TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers by Ho, Nguyen-Dong, Chang, Ik-Joon

“…Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to…”
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Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks by Mastropaolo, Antonio, Scalabrino, Simone, Cooper, Nathan, Nader Palacio, David, Poshyvanyk, Denys, Oliveto, Rocco, Bavota, Gabriele

“…Deep learning (DL) techniques are gaining more and more attention in the software engineering community…”
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On-Device Unsupervised Image Segmentation by Yang, Junhuan, Sheng, Yi, Zhang, Yuzhou, Jiang, Weiwen, Yang, Lei

“…Along with the breakthrough of convolutional neural networks, in particular encoder-decoder and U-Net, learning-based segmentation has emerged in many research…”
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Code Difference Guided Adversarial Example Generation for Deep Code Models by Tian, Zhao, Chen, Junjie, Jin, Zhi

“…Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex…”
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Emerging artificial intelligence methods in structural engineering by Salehi, Hadi, Burgueño, Rigoberto

“…•Potential research avenues for using AI methods in structural engineering are identified. Artificial intelligence (AI…”
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Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers by Stevens, Jacob R., Venkatesan, Rangharajan, Dai, Steve, Khailany, Brucek, Raghunathan, Anand

“…Transformers have transformed the field of natural language processing. Their superior performance is largely attributed to the use of stacked "self-attention"…”
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Modeling Programmer Attention as Scanpath Prediction by Bansal, Aakash, Su, Chia-Yi, Karas, Zachary, Zhang, Yifan, Huang, Yu, Li, Toby Jia-Jun, McMillan, Collin

“…This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people…”
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PETRI: Reducing Bandwidth Requirement in Smart Surveillance by Edge-Cloud Collaborative Adaptive Frame Clustering and Pipelined Bidirectional Tracking by Liu, Ruoyang, Zhang, Lu, Wang, Jingyu, Yang, Huazhong, Liu, Yongpan

“…Neural networks running on cloud servers have been widely used in smart surveillance, but they require high bandwidth to upload videos. Edge-cloud…”
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Artificial Intelligence and Evaluation: Emerging Technologies and Their Implications for Evaluation by Nielsen, Steffen Bohni, Rinaldi, Francesco Mazzeo, Petersson, Gustav Jakob

“…Artificial Intelligence and Evaluation: Emerging Technologies and Their Implications for Evaluation is a groundbreaking exploration of how the landscape of program evaluation will be redefined by artificial intelligence and other emerging…”
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Log Parsing: How Far Can ChatGPT Go? by Le, Van-Hoang, Zhang, Hongyu

“… In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks…”
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Neural Fields in Visual Computing and Beyond by Xie, Yiheng, Takikawa, Towaki, Saito, Shunsuke, Litany, Or, Yan, Shiqin, Khan, Numair, Tombari, Federico, Tompkin, James, sitzmann, Vincent, Sridhar, Srinath

“…Recent advances in machine learning have led to increased interest in solving visual computing problems using methods that employ coordinate…”
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FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control by Ye, Yutong, Zhao, Wupan, Wei, Tongquan, Hu, Shiyan, Chen, Mingsong

“…Although Reinforcement Learning (RL) has been successfully applied in traffic control, it suffers from the problems of high average vehicle travel time and…”
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Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast by Wu, Yawen, Wang, Zhepeng, Zeng, Dewen, Shi, Yiyu, Hu, Jingtong

“…After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive…”
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CURE: Code-Aware Neural Machine Translation for Automatic Program Repair by Jiang, Nan, Lutellier, Thibaud, Tan, Lin

“…Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to…”
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Studying Programmers Without Programming: Investigating Expertise Using Resting State fMRI by Karas, Zachary, Gold, Benjamin, Zhou, Violet, Reardon, Noah, Polk, Thad, Chang, Catie, Huang, Yu

“… In Cognitive Neuroscience, researchers commonly analyze resting-state data, in which participants' brain activity is recorded as they lay idle in the scanner…”
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Ansible Lightspeed: A Code Generation Service for IT Automation by Sahoo, Priyam, Pujar, Saurabh, Nalawade, Ganesh, Gebhardt, Richard, Mandel, Louis, Buratti, Luca

“…The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated…”
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On the Evaluation of Neural Code Translation: Taxonomy and Benchmark by Jiao, Mingsheng, Yu, Tingrui, Li, Xuan, Qiu, Guanjie, Gu, Xiaodong, Shen, Beijun

“…In recent years, neural code translation has gained increasing attention. While most of the research focuses on improving model architectures and training…”
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Testing Machine Translation via Referential Transparency by He, Pinjia, Meister, Clara, Su, Zhendong

“… To address this problem, we introduce referentially transparent inputs (RTIs), a simple, widely applicable methodology for validating…”
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On the Automation of Code Review Tasks Through Cross-Task Knowledge Distillation by Sghaier, Oussama Ben

“…Code review is essential for ensuring code quality and minimizing errors, but it is often complex, subjective, and time-consuming. Existing research treated…”
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A New Stochastic Computing Methodology for Efficient Neural Network Implementation by Canals, Vincent, Morro, Antoni, Oliver, Antoni, Alomar, Miquel L., Rossello, Josep L.

“…This paper presents a new methodology for the hardware implementation of neural networks (NNs…”
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