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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 ...
IEEE8d
Personalized Behavior Prediction with Encoder-to-Decoder Structure
The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also ...
C&EN8d
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 ... with a success rate of nearly 50%. Our model achieved competitive results, mainly ...
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 ...
Science Daily17d
All-in-one model reconstructs complex liver architecture
Liver disease modelling: This liver model reconstructs the liver periportal region architecture, is able to model aspects of cholestatic liver injury and biliary fibrosis, and can show how ...
IEEE18d
Cache Friendly Parallelization of Neural Encoder-Decoder Models Without Padding on Multi-core Architecture
In this paper, we implement and parallelize batch learning for a Sequence-to- Sequence (Seq2Seq) model, which is the most basic model of NMT, without using a padding strategy. More specifically, our ...