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+in+real-world+applications%252C+from+healthcare+to+natural+language+processing%252C+underscores+their+immense+utility+in+capturing+complex+relationships+within+high-dimensional+data.+However%252C+NNs+come+with+notable+disadvantages%252C+such+as+their+black-box+nature%252C+which+hampers+interpretability%252C+as+well+as+their+tendency+to+overfit+the+training+data.+We+introduce+a+novel+method+for+learning+interpretable+differentiable+logic+networks+(DLNs)+that+are+architectures+that+employ+multiple+layers+of+binary+logic+operators.+We+train+these+networks+by+softening+and+differentiating+their+discrete+components%252C+e.g.%252C+through+binarization+of+inputs%252C+binary+logic+operations%252C+and+connections+between+neurons.+This+approach+enables+the+use+of+gradient-based+learning+methods.+Experimental+results+on+twenty+classification+tasks+indicate+that+differentiable+logic+networks+can+achieve+accuracies+comparable+to+or+exceeding+that+of+traditional+NNs.+Equally+importantly%252C+these+networks+offer+the+advantage+of+interpretability.+Moreover%252C+their+relatively+simple+structure+results+in+the+number+of+logic+gate-level+operations+during+inference+being+up+to+a+thousand+times+smaller+than+NNs%252C+making+them+suitable+for+deployment+on+edge+devices.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.219399b5d17b353a03839e1f7737bfc0%3frik%3dSjuimsX2F%252fP%252fzw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Convolutional+Differentiable+Logic+Gate+Networks&simid=608023226899502911&FORM=IRPRST&ck=731E5A4D5900FC4DEDE7F65D8759C8CE&selectedIndex=4&itb=0)
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