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New Unified Qualcomm AI Stack Portfolio Revolutionizes Developer Access, Extends AI Leadership

New Unified Qualcomm AI Stack Portfolio Revolutionizes Developer Access, Extends AI Leadership
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Qualcomm Technologie , Ziad Asghar , Qualcomm Technologies Inc , Qualcomm Ai Software , Connected Intelligent , Qualcomm Technologies Powered , Connected Intelligent Edge , Qualcomm Technologies , Distinguished Engineer , Qualcomm Neural Processing , Efficiency Toolkit , Neural Architecture Search , Snapdragon Tech Summit , Google Cloud , Google Cloud Vertex ,

Qualcomm Announces Comprehensive AI Portfolio To Unlock Advanced AI Offerings

With this new announcement, a single AI software portfolio will work across Qualcomm Technologies-powered devices for the first time, spanning the wide range of Connected Intelligent Edge products – including mobile, automotive, XR, compute, IoT, and cloud platforms, , qualcomm, artificial intelligence ....

Qualcomm Technologie , Ziad Asghar , Qualcomm Technologies Inc , Qualcomm Technologies Powered , Connected Intelligent Edge , Qualcomm Technologies , Connected Intelligent , Efficiency Toolkit , Neural Architecture Search , Snapdragon Tech Summit , Google Cloud , Google Cloud Vertex , Qualcomm Neural Processing , Artificial Intelligence ,

Qualcomm offers unified AI stack for its phone, PC chips

Qualcomm offers unified AI stack for its phone, PC chips
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Ziad Asghar , Arm Based Snapdragon , Neural Architecture Search ,

"Multi-SelfGAN: A Self-Guiding Neural Architecture Search Method for Ge" by Jiachen Shi, Guoqiang Zhou et al.

In recent years, Reinforcement Learning and Gradient optimization were applied with Neural Architecture Search algorithms in Generative Adversarial Network to achieve their state-of-the-art (SOTA) performance. However, the existing RL-based methods utilised the calculation of Inception Score or Fréchet Inception Distance as the reward value to guide the controller, which actually wasted much of searching time. In order to improve the search efficiency without degradation of performance, this paper proposes recycling the discriminator to evaluate the performance of architectures, in other words, we propose to self-guide the search process. In the mean time, we introduce new concept of multiple controllers and the method of reward shaping to independently and effectively search the cell architectures. The experiments demonstrate the effectiveness and efficiency of our Multi-Self GAN and the ablation study also exhibits its robustness. ....

Generative Adversarial Network , Reinforcement Learning , Neural Architecture Search , Inception Score , Inception Distance , Computer Architecture , Generative Adversarial Network , Generative Adversarial Networks , Neural Architecture Search , Reinforcement Learning , Eward Shaping , Earch Problems ,