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Decoding high energy physics with AI and machine learning
In the world of particle physics, where scientists unravel the mysteries of the universe, artificial intelligence (AI) and ...
Meta’s new architecture helps robots interact in environments they’ve never seen before
It is Meta’s next step toward achieving “advanced machine intelligence,” and could be useful in manufacturing automation, surveillance analytics, in-building logistics, robotics, and other more ...
C&EN9d
Multisource Attention-Mechanism-Based Encoder–Decoder Model for Predicting Drug–Drug Interaction Events
To fill this gap, we propose two attention-mechanism-based encoder–decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can ...
Hugging Face Releases SmolVLA Open Source AI Model For Robotics Workflows
According to Hugging Face, advancements in robotics have been slow, despite the growth in the AI space. The company says that ...
IEEE18d
Cache Friendly Parallelization of Neural Encoder-Decoder Models Without Padding on Multi-core Architecture
In Machine Translation (MT), one of most important research fields of AI, models based on Recurrent Neural Net- works (RNN) show state-of-the-art performance in recent years, and many researchers keep ...
IEEE22d
Waste Image Segmentation Using Convolutional Neural Network Encoder-Decoder with SegNet Architecture
In this paper, we propose a waste segmentation method using Convolutional Neural Network based on the Encoder-Decoder approach ... setups of the architecture based on the number of filters in each ...