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"text": "regression on sarcos1-dataset . left : normalized mean squared errors ( the lower the better ) , middle : r-score ( the higher the better ) , right : running times ; as functions of nyström approximation level ( note the logartithmic scales ) . the results for krr are calculated without approximation and are shown as horizontal dashed lines . results for view and early fusion are worse than others and outside of the scope of the two plots .",
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"text": "What are the key features observed in this image?\n",
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"text": "regression on sarcos1-dataset . left : normalized mean squared errors ( the lower the better ) , middle : r-score ( the higher the better ) , right : running times ; as functions of nyström approximation level ( note the logartithmic scales ) . the results for krr are calculated without approximation and are shown as horizontal dashed lines . results for view and early fusion are worse than others and outside of the scope of the two plots .",
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"text": "regression on sarcos1-dataset . left : normalized mean squared errors ( the lower the better ) , middle : r-score ( the higher the better ) , right : running times ; as functions of nyström approximation level ( note the logartithmic scales ) . the results for krr are calculated without approximation and are shown as horizontal dashed lines . results for view and early fusion are worse than others and outside of the scope of the two plots .",
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"text": "regression on sarcos1-dataset . left : normalized mean squared errors ( the lower the better ) , middle : r-score ( the higher the better ) , right : running times ; as functions of nyström approximation level ( note the logartithmic scales ) . the results for krr are calculated without approximation and are shown as horizontal dashed lines . results for view and early fusion are worse than others and outside of the scope of the two plots .",
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"text": "strong scaling on cosmo large ( 69b particles ) , plasma large ( 189b particles ) and dayabay large ( 3b records ) datasets normalized to the time taken on 61 , 2576 , and 768 cores respectively . dashed lines denote ideal scaling .",
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"text": "strong scaling on cosmo large ( 69b particles ) , plasma large ( 189b particles ) and dayabay large ( 3b records ) datasets normalized to the time taken on 61 , 2576 , and 768 cores respectively . dashed lines denote ideal scaling .",
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"text": "strong scaling on cosmo large ( 69b particles ) , plasma large ( 189b particles ) and dayabay large ( 3b records ) datasets normalized to the time taken on 61 , 2576 , and 768 cores respectively . dashed lines denote ideal scaling .",
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"text": "strong scaling on cosmo large ( 69b particles ) , plasma large ( 189b particles ) and dayabay large ( 3b records ) datasets normalized to the time taken on 61 , 2576 , and 768 cores respectively . dashed lines denote ideal scaling .",
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"text": "1-nn exemplar classifier accuracy varying the number of ( counter- ) exemplars .",
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"text": "stream size vs. time , ǫ = 0.5 .",
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"text": "masstree characterization .",
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"text": "objective value obtained versus number of effective passes over data . in the plots , the “ best ” objective value is defined as the lowest value obtained across the methods .",
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"text": "objective value obtained versus number of effective passes over data . in the plots , the “ best ” objective value is defined as the lowest value obtained across the methods .",
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"text": "histogram statistics of cosine distance for positive and negative pairs on mnist [ 15 ] . feature discrimination and polymerization are more evident using coco than other losses .",
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"text": "histogram statistics of cosine distance for positive and negative pairs on mnist [ 15 ] . feature discrimination and polymerization are more evident using coco than other losses .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "histogram statistics of cosine distance for positive and negative pairs on mnist [ 15 ] . feature discrimination and polymerization are more evident using coco than other losses .",
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"text": "performance evaluation on general mer , using different numbers of latent topics in aeg .",
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"text": "comparison of the algorithms on exploration tasks ( a , b , c ) and goal-conditioned learning ( d ) , with parameters ξ = 0.1 , ζ = 0.7 , n = 10 ( a , b , d ) and ξ = 0.01 , ζ = 1 , n = 200 ( c ) . ( 9 % c.i . over 100 runs ( a , b ) , 40 runs ( c ) , 00 runs ( d ) ) . comparison of the solve time ( e ) achieved by column sum and dual formulations as a function of the number of variables .",
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"text": "comparison of the algorithms on exploration tasks ( a , b , c ) and goal-conditioned learning ( d ) , with parameters ξ = 0.1 , ζ = 0.7 , n = 10 ( a , b , d ) and ξ = 0.01 , ζ = 1 , n = 200 ( c ) . ( 9 % c.i . over 100 runs ( a , b ) , 40 runs ( c ) , 00 runs ( d ) ) . comparison of the solve time ( e ) achieved by column sum and dual formulations as a function of the number of variables .",
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"text": "comparison of the algorithms on exploration tasks ( a , b , c ) and goal-conditioned learning ( d ) , with parameters ξ = 0.1 , ζ = 0.7 , n = 10 ( a , b , d ) and ξ = 0.01 , ζ = 1 , n = 200 ( c ) . ( 9 % c.i . over 100 runs ( a , b ) , 40 runs ( c ) , 00 runs ( d ) ) . comparison of the solve time ( e ) achieved by column sum and dual formulations as a function of the number of variables .",
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"text": "comparison of the algorithms on exploration tasks ( a , b , c ) and goal-conditioned learning ( d ) , with parameters ξ = 0.1 , ζ = 0.7 , n = 10 ( a , b , d ) and ξ = 0.01 , ζ = 1 , n = 200 ( c ) . ( 9 % c.i . over 100 runs ( a , b ) , 40 runs ( c ) , 00 runs ( d ) ) . comparison of the solve time ( e ) achieved by column sum and dual formulations as a function of the number of variables .",
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"text": "the average training errors of the asyspa and the algorithm in [ 7 ] versus computing time under different β .",
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"text": "the average training errors of the asyspa and the algorithm in [ 7 ] versus computing time under different β .",
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"text": "the average training errors of the asyspa and the algorithm in [ 7 ] versus computing time under different β .",
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"text": "cumulative mismatch as a function of time for each consumer under three algorithms : ( a ) the proposed framework , ( b ) the proposed mpc and a myopic ( short-sighted ) settlement , and ( c ) a rule-based control algorithm and the myopic settlement . the proposed framework delivers smaller end-of year mismatches .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "performance of distributed ( dotted lines ) , centralized ( solid ) , and single-task learning ( dashed ) algorithms on benchmark datasets . shaded region shows standard error ( best viewed in color . ) .",
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"text": "performance of distributed ( dotted lines ) , centralized ( solid ) , and single-task learning ( dashed ) algorithms on benchmark datasets . shaded region shows standard error ( best viewed in color . ) .",
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"text": "for hellaswag , we ensure high human agreement through several rounds of annotation . by collecting how likely each ending is we can filter false negative endings – machine generations that sound realistic – and replace them with true negatives . on both subdatasets , bert performance increases during validation , but the gap to human performance remains wide .",
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"text": "for hellaswag , we ensure high human agreement through several rounds of annotation . by collecting how likely each ending is we can filter false negative endings – machine generations that sound realistic – and replace them with true negatives . on both subdatasets , bert performance increases during validation , but the gap to human performance remains wide .",
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"text": "for hellaswag , we ensure high human agreement through several rounds of annotation . by collecting how likely each ending is we can filter false negative endings – machine generations that sound realistic – and replace them with true negatives . on both subdatasets , bert performance increases during validation , but the gap to human performance remains wide .",
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"text": "clusters and mean series estimated by the regression mixture em algorithm applied with ( k , p ) = ( 2 , 10 ) .",
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