{"product_id":"symmetric-neural-networks-theory-paperback","title":"Symmetric Neural Networks Theory - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eSeymour L. Purvis\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSymmetric functions, which take as input an unordered, fixed-size s et, find practical application in myriad physical settings based on indistinguishable points or particles, and are also used as intermediate building blocks to construct networks with other invariances. Symmetric functions\u003c\/p\u003e\u003cp\u003eare known to be universally representable by neural networks that enforce permutation invariance. However the theoretical tools that characterize the approximation, optimization and generalization of typical networks fail to adequately characterize architectures that enforce invariance.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 142\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.3 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 13, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52494943387955,"sku":"9789810898106","price":38.32,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/bYdvS0Mzeu9789810898106.webp?v=1759996458","url":"https:\/\/www.vysn.com\/en-ca\/products\/symmetric-neural-networks-theory-paperback","provider":"VYSN","version":"1.0","type":"link"}