https://pinboard.in/u:Vaguery/public/ recent bookmarks from Vaguery 2026-05-24T17:27:04+00:00 https://arxiv.org/abs/2604.09723 Vaguery number-theory classification continued-fractions approximation can't-wait-to-understand-this rather-interesting https://pinboard.in/ https://pinboard.in/u:Vaguery/b:e52df2334e5f/ 2026-05-24T12:34:55+00:00 https://hal.science/hal-05580502v1 Vaguery numerical-methods continued-fractions approximation algorithms rather-interesting representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:726d350e5f0b/ 2026-05-24T12:22:36+00:00 https://inria.hal.science/hal-05593313v1 Vaguery numerical-methods computational-complexity algorithms rather-interesting special-cases to-write-about to-simulate approximation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:6249741238f1/ 2026-05-24T12:06:56+00:00 https://arxiv.org/abs/2302.06457 Vaguery probability-theory probabilistic-computing algorithms rather-interesting machine-learning engineering-design approximation to-write-about to-simulate https://pinboard.in/ https://pinboard.in/u:Vaguery/b:77469b3f11a6/ 2026-05-24T11:05:06+00:00 https://arxiv.org/abs/2501.10090 Vaguery continued-fractions approximation number-theory elegant-mathematics rather-interesting to-write-about representation consider:symbolic-regression https://pinboard.in/ https://pinboard.in/u:Vaguery/b:f73b97ad1bf4/ 2026-05-22T11:20:50+00:00 https://arxiv.org/abs/2502.12717 Vaguery machine-learning mathematical-programming combinatorics neural-networks formal-languages to-understand to-write-about group-theory rather-interesting feature-construction approximation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:062e950d111d/ 2026-01-21T12:46:20+00:00 https://arxiv.org/abs/2601.10667 Vaguery numerical-methods approximation rather-interesting algorithms continued-fractions representation to-write-about to-simulate consider:constructive-generalizations https://pinboard.in/ https://pinboard.in/u:Vaguery/b:f185ff1f75fd/ 2026-01-18T21:11:47+00:00 https://arxiv.org/abs/2510.16250 Vaguery approximation compressed-sensing compression neural-networks rather-interesting to-understand to-simulate consider:symbolic-regression consider:expression-trees https://pinboard.in/ https://pinboard.in/u:Vaguery/b:004d00cd5297/ 2025-12-31T17:56:32+00:00 https://arxiv.org/abs/2005.07678 Vaguery0. Our result completes a research direction set forth in the recent breakthrough paper [Chakraborty-Das-Goldenberg-Koucky-Saks, FOCS'18], which showed the first constant-factor approximation algorithm with a (strongly) sub-quadratic running time. The recent results of [Koucky-Saks, STOC'20] and [Brakensiek-Rubinstein, STOC'20] have shown near-linear time algorithms that obtain an additive approximation, near-linear in n (equivalently, constant-factor approximation when the edit distance value is close to n). In contrast, our algorithm obtains a constant-factor approximation in near-linear time for any input strings. In contrast to prior algorithms, which are mostly recursing over smaller substrings, our algorithm gradually smoothes out the local contribution to the edit distance over progressively larger substrings. To accomplish this, we iteratively construct a distance oracle data structure for the metric of edit distance on all substrings of input strings, of length niε for i=0,1,…,1/ε. The distance oracle approximates the edit distance over these substrings in a certain average sense, just enough to estimate the overall edit distance. ]]> strings computational-complexity algorithms rather-interesting approximation to-write-about to-implement consider:Push-code https://pinboard.in/ https://pinboard.in/u:Vaguery/b:71df08420968/ 2025-12-01T15:55:32+00:00 https://arxiv.org/abs/2308.06277 Vaguery boolean-networks neural-networks approximation rather-interesting nonlinear-dynamics complexology to-write-about to-simulate https://pinboard.in/ https://pinboard.in/u:Vaguery/b:d77e837ab688/ 2025-11-01T20:35:25+00:00 https://arxiv.org/abs/2509.01105 VaguerycH−u(ξ)q−v for all cubic irrationals ξ and rationals p/q. First, we transform this problem into one about the root separation of cubic polynomials. Second, we establish a conditional result: under the Hall conjecture (a special case of the famous abc-conjecture), every pair (u,v)=(3+2ϵ,5/2+ϵ) belongs to D3,1. Third, we show that every pair (u,v)∈D3,1 must satisfy u≥10−3v. Finally, we discuss an analogue of the set D3,1 in function fields where we are able to give a complete description. ]]> number-theory approximation numerical-methods rather-interesting continued-fractions to-write-about to-visualize consider:polynomial-approximations https://pinboard.in/ https://pinboard.in/u:Vaguery/b:f1bcb2412e97/ 2025-09-06T15:14:44+00:00 https://arxiv.org/abs/2508.19743 Vaguery nonlinear-dynamics continued-fractions approximation representation to-write-about to-simulate consider:functions-on-maps https://pinboard.in/ https://pinboard.in/u:Vaguery/b:ba3041fef470/ 2025-08-11T13:51:03+00:00 https://arxiv.org/abs/2209.15542 Vaguery number-theory approximation rational-numbers rather-interesting looking-to-see computational-complexity to-write-about to-simulate representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:73b9846f5700/ 2025-07-24T11:17:41+00:00 https://link.springer.com/article/10.1007/s11047-025-10028-7 Vaguery operations-research packing genetic-programming rather-interesting deterministic-approximations approximation machine-learning to-write-about consider:heuristic-move https://pinboard.in/ https://pinboard.in/u:Vaguery/b:19868cd2a237/ 2025-05-27T22:44:00+00:00 https://www.pnas.org/doi/10.1073/pnas.0710743106 Vaguery machine-learning approximation algorithms rather-interesting computational-complexity to-understand mathematical-programming metaheuristics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:d4e94025ab87/ 2024-12-21T17:43:43+00:00 https://arxiv.org/abs/2407.11533 Vaguery low-discrepancy-samples algorithms numerical-methods approximation rather-interesting to-write-about to-simulate combinatorics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:1f07890f97e1/ 2024-12-21T17:39:52+00:00 https://arxiv.org/abs/2408.06475 Vaguery low-discrepancy-numbers numerical-methods integration algorithms rather-interesting number-theory probability-theory to-understand to-simulate approximation sampling https://pinboard.in/ https://pinboard.in/u:Vaguery/b:f356a8ba7caa/ 2024-10-30T12:32:39+00:00 https://arxiv.org/abs/2404.05259 Vaguery neural-networks cellular-automata learning-by-watching recurrent-networks nonlinear-dynamics approximation machine-learning to-write-about to-simulate https://pinboard.in/ https://pinboard.in/u:Vaguery/b:39d241f4a9a0/ 2024-10-20T16:10:17+00:00 https://arxiv.org/abs/2306.16998 Vaguery discrepancy approximation numerical-methods randomness quasirandom-numbers monte-carlo-methods sampling visualization https://pinboard.in/ https://pinboard.in/u:Vaguery/b:ee82b8852476/ 2024-10-13T22:09:17+00:00 https://arxiv.org/abs/2203.14614 Vaguery distributed-processing asynchronous-behavior collective-behavior cellular-automata algorithms rather-interesting approximation to-write-about to-simulate consider:ACA-as-a-language https://pinboard.in/ https://pinboard.in/u:Vaguery/b:3966cfb54e9d/ 2024-10-08T19:58:32+00:00 https://arxiv.org/abs/2410.04264 Vaguery deep-learning neural-networks approximation rather-interesting to-understand hey-I-know-this-guy https://pinboard.in/ https://pinboard.in/u:Vaguery/b:4689073768de/ 2024-09-28T15:43:03+00:00 https://arxiv.org/abs/2210.14132 Vaguery rather-interesting p-adic-numbers cellular-automata to-understand image-processing approximation algorithms representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:32e459800586/ 2024-09-23T13:18:27+00:00 https://arxiv.org/abs/2402.18014 Vaguery approximation numerical-methods statistics risk rather-interesting portfolio-theory to-understand geometry error https://pinboard.in/ https://pinboard.in/u:Vaguery/b:2b593313b435/ 2024-09-21T13:58:51+00:00 https://arxiv.org/abs/2207.13944 Vaguery probability-theory number-theory approximation rather-interesting to-simulate consider:lexicase-selection consider:numerical-modeling consider:quasirandom-numbers https://pinboard.in/ https://pinboard.in/u:Vaguery/b:5a4d9bfcab6c/ 2024-08-02T01:19:43+00:00 https://arxiv.org/abs/2111.10722 Vaguery machine-learning discrepancy-theory approximation sampling really-very-interesting to-write-about consider:combinatorics consider:algorithmic-information-theory https://pinboard.in/ https://pinboard.in/u:Vaguery/b:b9864fa33c22/ 2024-03-31T00:33:08+00:00 https://arxiv.org/abs/2402.17764 Vaguery neural-networks compression rather-interesting representation approximation huh-wouldja-lookit-that https://pinboard.in/ https://pinboard.in/u:Vaguery/b:4707a0ccd457/ 2023-10-10T10:04:37+00:00 https://arxiv.org/abs/2209.01147 Vaguery0 and (X,) with dual shatter function π∗(k)=O(kd), returns a coloring with expected discrepancy O(|X|1−1/dlog||‾‾‾‾‾‾‾‾‾‾‾‾‾‾√) (this bound is tight) in time Õ(||⋅|X|1/d+|X|2+1/d), improving upon the previous-best time of O(||⋅|X|3) by at least a factor of |X|2−1/d when ||≥|X|. This setup includes many geometric classes, families of bounded dual VC-dimension, and others. As an immediate consequence, we obtain an improved algorithm to construct ε-approximations of sub-quadratic size. Our method uses primal-dual reweighing with an improved analysis of randomly updated weights and exploits the structural properties of the set system via matchings with low crossing number -- a fundamental structure in computational geometry. In particular, we get the same |X|2−1/d factor speed-up on the construction time of matchings with crossing number O(|X|1−1/d), which is the first improvement since the 1980s. The proposed algorithms are very simple, which makes it possible, for the first time, to compute colorings with near-optimal discrepancies and near-optimal sized approximations for abstract and geometric set systems in dimensions higher than 2. ]]> low-discrepancy-samples sampling approximation algorithms rather-interesting to-understand to-visualize to-write-about https://pinboard.in/ https://pinboard.in/u:Vaguery/b:2c854195ad31/ 2023-09-30T12:46:11+00:00 https://arxiv.org/abs/2209.14440 Vaguery machine-learning probability-theory rather-interesting metrics distance numerical-methods approximation neural-networks consider:looking-to-see consider:representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:2550a216a21d/ 2023-09-09T22:36:45+00:00 https://arxiv.org/abs/2109.10529 Vaguery continued-fractions approximation numerical-methods rather-interesting to-understand representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:3a16dc1b8540/ 2023-09-09T22:35:16+00:00 https://arxiv.org/abs/2109.07838 Vaguery numerical-methods floating-point approximation error rather-interesting https://pinboard.in/ https://pinboard.in/u:Vaguery/b:e1c6d71a6a09/ 2023-09-09T13:01:14+00:00 https://arxiv.org/abs/2308.04825 Vaguery low-discrepancy-sequence numerical-methods sampling algorithms horse-races rather-interesting approximation performance-measure nonlinear-dynamics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:1d714b506f44/ 2023-08-19T22:16:45+00:00 https://arxiv.org/abs/2002.06118 Vaguery computational-geometry low-discrepancy-numbers approximation looking-to-see geometry https://pinboard.in/ https://pinboard.in/u:Vaguery/b:0df86e75f223/ 2023-08-19T21:39:09+00:00 https://arxiv.org/abs/2102.07833 Vaguery numerical-methods low-discrepancy-numbers algorithms tutorial approximation rather-interesting https://pinboard.in/ https://pinboard.in/u:Vaguery/b:a246e9d597c9/ 2023-08-19T14:16:28+00:00 https://arxiv.org/abs/2210.16548 Vaguery approximation numerical-methods low-discrepancy quasirandom-numbers rather-interesting heuristics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:ded4a361ce8c/ 2023-02-07T13:03:55+00:00 https://arxiv.org/abs/2110.14466 Vaguery number-theory information-theory rather-interesting representation continued-fractions approximation heuristics to-understand to-simulate https://pinboard.in/ https://pinboard.in/u:Vaguery/b:29fd6e3eb76f/ 2023-02-07T13:01:32+00:00 https://arxiv.org/abs/2109.08962 Vaguery number-theory enumeration continued-fractions rather-interesting to-understand representation approximation optimization computational-complexity https://pinboard.in/ https://pinboard.in/u:Vaguery/b:2d5320455881/ 2023-01-01T13:09:52+00:00 https://arxiv.org/abs/2203.01794 Vaguery0 is an arbitrarily small constant, such that given any segment ab⎯⎯⎯⎯⎯⎯ and two points s,t∈P we can compute the Fréchet distance between ab⎯⎯⎯⎯⎯⎯ and the curve of P in between s and t in O((n/k)log2n+log4n) time. This is the first result that allows efficient exact Fréchet distance queries for arbitrarily oriented segments. We also present two applications of our data structure: we show that we can compute a local δ-simplification (with respect to the Fréchet distance) of a polygonal curve in O(n5/2+ε) time, and that we can efficiently find a translation of an arbitrary query segment ab⎯⎯⎯⎯⎯⎯ that minimizes the Fréchet distance with respect to a subcurve of P. ]]> computational-geometry computational-complexity algorithms distance preprocessing approximation rather-interesting to-simulate to-write-about consider:oracles https://pinboard.in/ https://pinboard.in/u:Vaguery/b:5471ba660ae6/ 2022-07-09T12:40:45+00:00 https://arxiv.org/abs/2106.04900 Vaguery modeling approximation machine-learning neural-networks 20-years-ago-we-did-this-in-GP to-write-about https://pinboard.in/ https://pinboard.in/u:Vaguery/b:d2886cc4e68b/ 2022-05-19T10:41:29+00:00 https://arxiv.org/abs/1808.00572 Vaguery number-theory primes approximation continuous-and-discrete-models rather-interesting signal-processing https://pinboard.in/ https://pinboard.in/u:Vaguery/b:0cadfb1287de/ 2022-05-14T10:53:18+00:00 https://arxiv.org/abs/1801.07661 Vaguery analog-computing representation approximation to-understand proof nonlinear-dynamics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:8833ee7868c5/ 2022-04-30T21:50:09+00:00 https://arxiv.org/abs/2106.13914 Vaguery approximation neural-networks representation floating-point rather-interesting to-write-about to-simulate consider:lexicase consider:non-gradient-applications https://pinboard.in/ https://pinboard.in/u:Vaguery/b:9c2dfdede684/ 2022-04-19T10:30:54+00:00 https://arxiv.org/abs/2102.02365 Vaguery approximation inverse-problems rather-interesting learning-from-data nonlinear-dynamics online-learning to-understand statistics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:3fa7289011de/ 2022-03-17T10:52:01+00:00 https://arxiv.org/abs/2105.07512 Vaguery image-processing neural-networks generative-models approximation compression to-write-about to-visualize consider:performance-measures machine-learning computational-complexity https://pinboard.in/ https://pinboard.in/u:Vaguery/b:e52739abcbfa/ 2022-03-04T11:21:16+00:00 https://arxiv.org/abs/1906.05746 Vaguery approximation models-and-modes representation optimization system-identification tensors to-understand https://pinboard.in/ https://pinboard.in/u:Vaguery/b:4cc8017689df/ 2022-02-21T09:23:15+00:00 https://arxiv.org/abs/2110.14207 Vaguery natural-language-processing artificial-intelligence rather-interesting define-your-terms benchmarking approximation to-write-about consider:representation consider:metaheuristics https://pinboard.in/ https://pinboard.in/u:Vaguery/b:d5af6eaea474/ 2022-02-13T12:37:09+00:00 https://arxiv.org/abs/1704.00362 Vaguery machine-learning nonuniformity rather-interesting concept-drift approximation time-series to-write-about to-simulate consider:in-browser-data-sources https://pinboard.in/ https://pinboard.in/u:Vaguery/b:e52f7be507d3/ 2022-02-12T13:26:09+00:00 https://arxiv.org/abs/1803.00250 Vaguery machine-learning representation rather-interesting probability-theory to-understand approximation consider:EDAs consider:performance-measures https://pinboard.in/ https://pinboard.in/u:Vaguery/b:89b7794363b0/ 2022-02-08T12:29:44+00:00 https://arxiv.org/abs/2001.11692 Vaguery edit-distance neural-networks approximation heuristics rather-interesting to-understand nudge-targets https://pinboard.in/ https://pinboard.in/u:Vaguery/b:52092f6a70f1/ 2022-02-05T13:36:32+00:00 https://arxiv.org/abs/2106.14274 Vaguery computational-geometry numerical-methods neural-networks machine-learning mesh-generation 3d optimization rather-interesting approximation to-write-about consider:performance-measures consider:edge-cases https://pinboard.in/ https://pinboard.in/u:Vaguery/b:7f9a89ab9655/ 2022-01-22T13:10:00+00:00 https://arxiv.org/abs/2110.14151 Vaguery algorithms approximation numerical-methods 3d rather-interesting optimization to-write-about to-understand consider:visualization granular-materials https://pinboard.in/ https://pinboard.in/u:Vaguery/b:20ffc45d5548/ 2022-01-02T21:20:08+00:00 https://arxiv.org/abs/1910.11826 Vaguery numerical-methods optimization rather-interesting approximation algorithms to-understand https://pinboard.in/ https://pinboard.in/u:Vaguery/b:9eb87d8d8377/ 2022-01-01T13:29:33+00:00 https://arxiv.org/abs/1511.02179 Vaguery number-theory representation rather-interesting algorithms approximation to-write-about https://pinboard.in/ https://pinboard.in/u:Vaguery/b:61f369c4e67d/ 2022-01-01T13:22:30+00:00 https://arxiv.org/abs/1207.1067 Vaguery continued-fractions number-theory approximation rather-interesting dynamical-systems to-write-about to-simulate consider:stochastic-search https://pinboard.in/ https://pinboard.in/u:Vaguery/b:a28f68ffe2fe/ 2022-01-01T13:08:59+00:00 https://arxiv.org/abs/2106.10712 Vaguery nonlinear-dynamics continued-fractions approximation rather-interesting number-theory to-write-about to-simulate consider:random-approximations optimization https://pinboard.in/ https://pinboard.in/u:Vaguery/b:f63fc99721fd/ 2021-10-23T06:50:26+00:00 https://arxiv.org/abs/2003.07798 Vaguery nonlinear-dynamics approximation numerical-methods rather-interesting dynamical-systems to-understand system-identification https://pinboard.in/ https://pinboard.in/u:Vaguery/b:69ce0d534dcf/ 2021-10-23T06:47:31+00:00 https://arxiv.org/abs/2003.02605 Vaguery0, assuming that the coordinates of all input objects are in [0,N]d and each of their edges has length at least 1. We obtain the following results: ∙ For weighted intervals, we maintain a (1+ϵ)-approximate solution. ∙ For d-dimensional hypercubes we maintain a (1+ϵ)2d-approximate solution in the unweighted case and a O(2d)-approximate solution in the weighted case. Also, we show that for maintaining an unweighted (1+ϵ)-approximate solution one needs polynomial update time for d≥2 if the ETH holds. ∙ For weighted d-dimensional hyperrectangles we present a dynamic algorithm with approximation ratio (1+ϵ)logd−1N. ]]> computational-complexity computational-geometry multiobjective-optimization rather-interesting to-write-about to-simulate consider:genetic-programming approximation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:4cb39f59b01a/ 2021-10-06T11:00:16+00:00 https://arxiv.org/abs/2109.11003 Vaguery number-theory approximation continued-fractions to-write-about to-simulate consider:constraint-satisfaction consider:locked-values https://pinboard.in/ https://pinboard.in/u:Vaguery/b:52d190d6ab3d/ 2021-07-03T11:09:19+00:00 https://arxiv.org/abs/1009.5202 Vaguery number-theory continued-fractions approximation rather-interesting https://pinboard.in/ https://pinboard.in/u:Vaguery/b:5e364145023f/ 2021-06-27T10:12:23+00:00 https://arxiv.org/abs/2012.09715 Vaguery numerical-methods approximation rather-interesting simulation sampling performance-measure computational-complexity nudge-targets consider:representation consider:performance-measures consider:lexicase consider:extreme-values https://pinboard.in/ https://pinboard.in/u:Vaguery/b:8ed61cde1044/ 2021-06-18T13:49:19+00:00 https://www.quantamagazine.org/how-rational-math-catches-slippery-irrational-numbers-20200310/ Vaguery approximation number-theory algorithms nudge-targets consider:genetic-programming consider:worst-cases consider:representation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:371c7e446424/ 2021-06-07T10:52:32+00:00 https://arxiv.org/abs/1802.03482 Vaguery machine-learning heuristics approximation proxy-problems rather-interesting discrete-mathematics to-write-about consider:visualization consider:feature-discovery https://pinboard.in/ https://pinboard.in/u:Vaguery/b:d91e1255bca1/ 2021-05-22T21:50:23+00:00 https://arxiv.org/abs/2012.05503 Vaguery dynamical-systems reaction-networks simulation algorithms approximation sampling rather-interesting to-write-about consider:general-case consider:ReQ systems-biology https://pinboard.in/ https://pinboard.in/u:Vaguery/b:59ee2e9eec9c/ 2021-05-19T11:29:08+00:00 https://arxiv.org/abs/2102.01161 Vaguery computer-vision optimization approximation data-fusion rather-interesting to-write-about to-simulate consider:conflict-areas consider:anomaly-detection machine-learning neural-networks https://pinboard.in/ https://pinboard.in/u:Vaguery/b:08b400e783a4/ 2021-03-28T12:20:56+00:00 https://arxiv.org/abs/2103.01614 Vaguery numerical-methods rather-interesting finite-elements-analysis algorithms computational-geometry approximation to-understand consider:performance-measures consider:visualization how-hard-to-show-in-browser? https://pinboard.in/ https://pinboard.in/u:Vaguery/b:4e5f1293d09d/ 2020-11-29T15:21:01+00:00 https://arxiv.org/abs/2009.00295 Vaguery simulation fluid-dynamics representation looking-to-see rather-interesting modeling-is-not-mathematics to-write-about consider:simulation approximation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:a4de464b0f00/ 2020-11-15T11:40:45+00:00 https://arxiv.org/abs/2010.11406 Vaguery matrices inverse-problems rather-interesting approximation relaxations-of-rules to-write-about consider:looking-to-see consider:metaheuristics constraint-satisfaction multiobjective-optimization https://pinboard.in/ https://pinboard.in/u:Vaguery/b:ae9cc8c70f6c/ 2020-08-05T17:38:53+00:00 https://arxiv.org/abs/1907.00205 Vaguery continued-fractions metaheuristics approximation rather-interesting experimental-mathematics proof-systems to-write-about to-simulate consider:representation consider:performance-measures consider:genetic-programming https://pinboard.in/ https://pinboard.in/u:Vaguery/b:1af9f125d57f/ 2020-07-13T13:32:58+00:00 https://arxiv.org/abs/1908.09791 Vaguery1019) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently. On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and CO2 emission. In particular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under the mobile setting (<600M MACs). OFA is the winning solution for the 3rd Low Power Computer Vision Challenge (LPCVC), DSP classification track and the 4th LPCVC, both classification track and detection track. Code and 50 pre-trained models (for many devices & many latency constraints) are released at this https URL. ]]> machine-learning generalization sustainability rather-interesting to-write-about to-do consider:genetic-programming consider:once-and-for-all-GP transfer-learning approximation consider:performance-measures https://pinboard.in/ https://pinboard.in/u:Vaguery/b:fa59c5daa6a0/ 2020-05-26T11:36:10+00:00 https://arxiv.org/abs/1909.03152 Vaguery tutorial graph-theory approximation heuristics rather-interesting to-write-about to-simulate consider:random-guesses consider:relaxation https://pinboard.in/ https://pinboard.in/u:Vaguery/b:9e7af9693e7f/ 2020-05-18T21:18:32+00:00 https://arxiv.org/abs/1803.04221 Vaguery0 independent of (W1,W2). Focusing on the presence and strength of asymptotic tail dependence, as expressed through commonly-used summary parameters, broad factors that affect the results are: the heaviness of the tails of R and (W1,W2), the shape of the support of (W1,W2), and dependence between (W1,W2). When R is distinctly lighter tailed than (W1,W2), the extremal dependence of (X1,X2) is typically the same as that of (W1,W2), whereas similar or heavier tails for R compared to (W1,W2) typically result in increased extremal dependence. Similar tail heavinesses represent the most interesting and technical cases, and we find both asymptotic independence and dependence of (X1,X2) possible in such cases when (W1,W2) exhibit asymptotic independence. The bivariate case often directly extends to higher-dimensional vectors and spatial processes, where the dependence is mainly analyzed in terms of summaries of bivariate sub-vectors. The results unify and extend many existing examples, and we use them to propose new models that encompass both dependence classes. ]]> probability-theory representation bivariate-distributions correlation simulation approximation rather-interesting to-understand to-simulate https://pinboard.in/ https://pinboard.in/u:Vaguery/b:98f5d851db63/