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Integrating AI into Your Finance Function

Emerging technologies such as artificial intelligence and machine learning have transformed the traditional finance function by making processes efficient, improving accuracy, and enabling data-driven decision-making. According to a Forrester survey, 98% of financial institutions believe that AI and ML can give them an edge and...

Solving Unsolvable Combinatorial Problems with AI

This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How Qualcomm AI Research is optimizing hardware-specific compilers and chip design with AI Combinatorial problems are all around us. When we are faced with many choices and...

Open Data: Unleashing Opportunities and Challenges

The below is a summary of my article on Open Data as published on Open data, a concept rapidly gaining importance in our increasingly data-driven world, refers to freely accessible and usable data by anyone without restrictions, be it copyrights, patents, or other control...

(ML) MobileNetV2: Inverted Residuals and Linear Bottlenecks | by YEN HUNG CHENG | Jun, 2023

Photo by Bagus Hernawan on Unsplash在 MobileNetV2 中,作者使用了倒置殘差結構,其中快速連接位於狹窄的瓶頸層之間。他們採用輕量級深度卷積來過濾非線性源的特徵。同時,他們發現去除窄層中的非線性以保持表徵能力是非常重要的,並證明了這一改進可以提高性能。Depthwise Separable ConvolutionsLinear Bottlenecks簡單來說,Linear Bottlenecks 就是指在 MobileNetV1 最後做完 1x1 convolution 將激活函數 ReLU 拿掉,而為什麽這麼做,可以下方的 manifold of interest 介紹。manifold of interest在神經網絡中,每一層都會對輸入數據進行一些操作,比如卷積、激活、池化等等。這些操作產生的輸出值就被稱為該層的激活值。對於一個真實圖像的輸入集,每個層的激活值都形成了一個集合。我們可以把每個集合看作是一個流形(manifold)。這些流形反映了圖像的特徵,比如邊緣、紋理、形狀等等。長期以來,人們一直認為神經網絡中感興趣的流形可以嵌入到低維子空間中。MobileNetV1 成功利用,通過寬度乘數參數在計算和精度之間進行有效權衡。 按照這種直覺,寬度乘法器方法允許減少激活空間的維數,直到感興趣的流形跨越整個空間。然而,當我們回憶起深度卷積神經網絡實際上具有非線性的坐標變換(例如 ReLU)時,這種直覺就會失效。Examples of ReLU transformations of low-dimensional manifolds embedded in higher-dimensional spaces一開始在 2 維空間上建立一個 manifold of interest ,接下來會通過隨機矩陣...

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