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BishopLanguage Model
%252C+a+novel+end-to-end+framework+for+deep+tabular+learning.+BiSHop+handles+the+two+major+challenges+of+deep+tabular+learning%3a+non-rotationally+invariant+data+structure+and+feature+sparsity+in+tabular+data.+Our+key+motivation+comes+from+the+recent+established+connection+between+associative+memory+and+attention+mechanisms.+Consequently%252C+BiSHop+uses+a+dual-component+approach%252C+sequentially+processing+data+both+column-wise+and+row-wise+through+two+interconnected+directional+learning+modules.+Computationally%252C+these+modules+house+layers+of+generalized+sparse+modern+Hopfield+layers%252C+a+sparse+extension+of+the+modern+Hopfield+model+with+adaptable+sparsity.+Methodologically%252C+BiSHop+facilitates+multi-scale+representation+learning%252C+capturing+both+intra-feature+and+inter-feature+interactions%252C+with+adaptive+sparsity+at+each+scale.+Empirically%252C+through+experiments+on+diverse+real-world+datasets%252C+we+demonstrate+that+BiSHop+surpasses+current+SOTA+methods+with+significantly+less+HPO+runs%252C+marking+it+a+robust+solution+for+deep+tabular+learning.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2d4c6b97f90eea56aa43893913de5b72%3frik%3dgsY690vnoamjYQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Bishop+Language+Model&FORM=IRPRST&ck=283CD429F6852A9920854166FD9E59AC&selectedIndex=22&itb=0)
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