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{"poster":"Boujee Stripper","date":"2016-10-19T21:34:06.884+0000","title":"How to urgently get my ticket resolved?","subforum":"[ARCHIVED] Help & Support","up_votes":1,"down_votes":0,"body":"Hi, my main account was hacked not too long ago and made several tickets/posts but soon i will start to elo decay and i dont want to drop my rank. The person has not logged in or removed anyone on friends list, but i havea unique name and am afriad of the account being sold, name being sold, or decaying soon. How can i get riots attention quicker? or do i just have to wait it out? i sent my email 3 days ago and im starting to worry how long it will take to resolve. If riot has more questions and i have to email more responses, this can take over a week to resolve and im fearing the worst. I spent too much time on this acc and just worried for the worst. Please help soon","replies":[{"poster":"Porocles","date":"2016-10-19T21:50:48.228+0000","up_votes":1,"down_votes":0,"body":"There's no ticket expedition, and you'll receive a response in the order that it came as well as depending on the size of the queue. I looked into this for you and do see quite a few tickets on this, so a few will likely be closed to prevent delays or confusion towards your case. Your ticket has been escalated to our account team, and they'll reach out to you as soon as possible. Unfortunately, any ranked decay that would occur can't be disabled so it may be necessary to replay some ranked. Hang tight, and we'll help as soon as we can!","replies":[{"poster":"Boujee Stripper","date":"2016-10-19T22:42:34.242+0000","up_votes":1,"down_votes":0,"body":"ah, what are the steps that the tickets take to process if its up to account team now? like is t here a template/layout for the process?","replies":[{"poster":"Porocles","date":"2016-10-19T22:44:08.504+0000","up_votes":1,"down_votes":0,"body":"Usually Blitzbot will be the first to respond, because in most cases he can automatically help a player with their account recovery. More sensitive cases or those where not enough account verification was provided would then be sent to our account team to be reviewed personally. Great question, and I hope that helps clear things up!","replies":[{"poster":"Boujee Stripper","date":"2016-10-20T01:19:19.511+0000","up_votes":1,"down_votes":0,"body":"this does give some helpful info, but by my ticket being sent to the accounts team means its a personal? if thats so, im hoping so because i provided a surplus of evidence over my 4 years that the account is mine and plenty of screenshots. its been 3 days already and i wanted to know, if they have anything else to say, would my email be sent back UP the queue so i have to wait a while for a reply? or would it be sent back as a priority as an open case?","replies":[{"poster":"Porocles","date":"2016-10-20T15:37:18.736+0000","up_votes":1,"down_votes":0,"body":"Once a specialist responds to your ticket, you'll be in direct contact with them and no longer in queue. You'll be able to regularly correspond with your specialist until everything is resolved!","replies":[{"poster":"Boujee Stripper","date":"2016-10-20T19:25:20.596+0000","up_votes":1,"down_votes":0,"body":"is there any way you can check how much longer the queue is for me? its been 4 days >.< riots never taken this long and at such a sensitive time its killing me","replies":[]}]}]},{"poster":"Boujee Stripper","date":"2016-10-20T01:33:44.040+0000","up_votes":1,"down_votes":0,"body":"also one last thing, what are the hours of the support center? i just want to know when i should stop staring at my email and just go to bed lol","replies":[]}]}]}]}]} |
{
"directions": [
"Preheat the oven to 375 degrees F (190 degrees C). Season rabbit bones with mirepoix base, and place in a 9x13 inch baking dish or similar. Roast for 30 minutes, or until browned and fragrant.",
"Remove rabbit bones to a saucepan, and add enough water to cover by about 1 inch. Bring to a boil, then cook over medium-high heat until the liquid is reduced by half to provide a stock for the recipe. This will take up to 30 minutes depending on the size of your pan.",
"Mix the flour, salt, and pepper. Coat rabbit pieces with the seasoned flour. Heat 1 tablespoon of oil in the dish used to bake the rabbit bones. Cook rabbit pieces over medium-high heat, or in the oven, just until evenly browned on the outside.",
"Remove rabbit pieces, and add the carrots, onion, leek, turnip and potatoes. Add bacon, and if necessary, a little more oil. Place the rabbit pieces over the vegetables. Mix together your homemade rabbit stock and tomato puree; pour into the baking dish. Cover tightly with aluminum foil or a lid. Reduce the oven temperature to 350 degrees F (175 degrees C).",
"Bake the rabbit casserole for about 1 hour, or until rabbit is cooked through. Adjust the seasonings to taste. If you wish to use the chocolate, mix it in at this time.",
"Heat 2 tablespoons of oil in a large skillet over medium-high heat. Trim the crusts from the bread slices, and slice in half diagonally or into cubes. Fry bread in oil until lightly browned.",
"Serve casserole in the pan, topped with fried bread (or croutons) and sprinkled with chopped parsley."
],
"ingredients": [
"2 (2 pound) rabbits, dressed and deboned, bones reserved",
"1 tablespoon mirepoix base",
"1/2 cup all-purpose flour",
"salt and freshly ground black pepper to taste",
"1 tablespoon vegetable oil",
"2 carrots, diced",
"1/2 onion, chopped",
"1 leek, chopped",
"1 turnip, diced",
"2 medium potatoes - peeled and cubed",
"1/2 pound smoked bacon, cubed",
"1 tablespoon tomato puree",
"3 (1 ounce) squares bittersweet chocolate, chopped (optional)",
"2 tablespoons vegetable oil",
"3 slices white bread",
"1 tablespoon chopped fresh parsley"
],
"language": "en-US",
"source": "allrecipes.com",
"tags": [],
"title": "Rabbit Casserole",
"url": "http://allrecipes.com/recipe/68355/rabbit-casserole/"
}
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[
"ויקיציטוט",
"חכם",
"חיל שריון",
"אלגוריה",
"מוזיקה",
"ציור",
"יהדות",
"קרן ויקימדיה",
"אל",
"האקדמיה ללשון העברית",
"מציאות",
"בית ספר יסודי",
"עתיד",
"ויקישיתוף",
"קונפוציוס",
"אמונה",
"ספריית קלסוס",
"ידע",
"תכונה (אישיות)",
"אפלטון",
"סופיה",
"כישלון",
"1754",
"ברק",
"היסק",
"ספר אחיקר החכם",
"צבא",
"יעקב דה ויט",
"זקנה",
"היקש",
"עץ",
"בית חרושת",
"חשיבה",
"היררכיה",
"זמן",
"אסיה הקטנה",
"חכמת שלמה",
"מיסטיקה",
"מוצר",
"הסקת מסקנות",
"כתיב חסר ניקוד",
"דת",
"בקבוק",
"מיתולוגיה יוונית",
"ניסיון",
"דעת (פירושונים)",
"נצרות",
"הינדואיזם",
"בן סירא",
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timeline\ndate:\nlayout: timeline\n---","updated":"2017-07-15T12:21:30.000Z","path":"timeline/index.html","comments":1,"_id":"cj559yovy0004tum7vnsl5pms","content":"","site":{"data":{}},"excerpt":"","more":""}],"Post":[{"title":"Hello World","_content":"Welcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/deployment.html)\n","source":"_posts/hello-world.md","raw":"---\ntitle: Hello World\n---\nWelcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/deployment.html)\n","slug":"hello-world","published":1,"date":"2017-07-15T10:20:01.000Z","updated":"2017-07-15T10:20:01.000Z","comments":1,"layout":"post","photos":[],"link":"","_id":"cj559yosy0001tum7abnl9iqk","content":"<p>Welcome to <a href=\"https://hexo.io/\" target=\"_blank\" rel=\"external\">Hexo</a>! This is your very first post. Check <a href=\"https://hexo.io/docs/\" target=\"_blank\" rel=\"external\">documentation</a> for more info. If you get any problems when using Hexo, you can find the answer in <a href=\"https://hexo.io/docs/troubleshooting.html\" target=\"_blank\" rel=\"external\">troubleshooting</a> or you can ask me on <a href=\"https://github.com/hexojs/hexo/issues\" target=\"_blank\" rel=\"external\">GitHub</a>.</p>\n<h2 id=\"Quick-Start\"><a href=\"#Quick-Start\" class=\"headerlink\" title=\"Quick Start\"></a>Quick Start</h2><h3 id=\"Create-a-new-post\"><a href=\"#Create-a-new-post\" class=\"headerlink\" title=\"Create a new post\"></a>Create a new post</h3><pre class=\" language-bash\"><code class=\"language-bash\">$ hexo new <span class=\"token string\">\"My New Post\"</span>\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/writing.html\" target=\"_blank\" rel=\"external\">Writing</a></p>\n<h3 id=\"Run-server\"><a href=\"#Run-server\" class=\"headerlink\" title=\"Run server\"></a>Run server</h3><pre class=\" language-bash\"><code class=\"language-bash\">$ hexo server\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/server.html\" target=\"_blank\" rel=\"external\">Server</a></p>\n<h3 id=\"Generate-static-files\"><a href=\"#Generate-static-files\" class=\"headerlink\" title=\"Generate static files\"></a>Generate static files</h3><pre class=\" language-bash\"><code class=\"language-bash\">$ hexo generate\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/generating.html\" target=\"_blank\" rel=\"external\">Generating</a></p>\n<h3 id=\"Deploy-to-remote-sites\"><a href=\"#Deploy-to-remote-sites\" class=\"headerlink\" title=\"Deploy to remote sites\"></a>Deploy to remote sites</h3><pre class=\" language-bash\"><code class=\"language-bash\">$ hexo deploy\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/deployment.html\" target=\"_blank\" rel=\"external\">Deployment</a></p>\n","site":{"data":{}},"excerpt":"","more":"<p>Welcome to <a href=\"https://hexo.io/\" target=\"_blank\" rel=\"external\">Hexo</a>! This is your very first post. Check <a href=\"https://hexo.io/docs/\" target=\"_blank\" rel=\"external\">documentation</a> for more info. If you get any problems when using Hexo, you can find the answer in <a href=\"https://hexo.io/docs/troubleshooting.html\" target=\"_blank\" rel=\"external\">troubleshooting</a> or you can ask me on <a href=\"https://github.com/hexojs/hexo/issues\" target=\"_blank\" rel=\"external\">GitHub</a>.</p>\n<h2 id=\"Quick-Start\"><a href=\"#Quick-Start\" class=\"headerlink\" title=\"Quick Start\"></a>Quick Start</h2><h3 id=\"Create-a-new-post\"><a href=\"#Create-a-new-post\" class=\"headerlink\" title=\"Create a new post\"></a>Create a new post</h3><pre><code class=\"bash\">$ hexo new "My New Post"\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/writing.html\" target=\"_blank\" rel=\"external\">Writing</a></p>\n<h3 id=\"Run-server\"><a href=\"#Run-server\" class=\"headerlink\" title=\"Run server\"></a>Run server</h3><pre><code class=\"bash\">$ hexo server\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/server.html\" target=\"_blank\" rel=\"external\">Server</a></p>\n<h3 id=\"Generate-static-files\"><a href=\"#Generate-static-files\" class=\"headerlink\" title=\"Generate static files\"></a>Generate static files</h3><pre><code class=\"bash\">$ hexo generate\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/generating.html\" target=\"_blank\" rel=\"external\">Generating</a></p>\n<h3 id=\"Deploy-to-remote-sites\"><a href=\"#Deploy-to-remote-sites\" class=\"headerlink\" title=\"Deploy to remote sites\"></a>Deploy to remote sites</h3><pre><code class=\"bash\">$ hexo deploy\n</code></pre>\n<p>More info: <a href=\"https://hexo.io/docs/deployment.html\" target=\"_blank\" rel=\"external\">Deployment</a></p>\n"}],"PostAsset":[],"PostCategory":[],"PostTag":[],"Tag":[]}} |
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{
"author": "TiFYW75zz",
"id": "DoBOmVTbS",
"title": "ГЛЕК",
"link": "metrs_poem.php?poem=7508",
"html": "\n<h4></h4>\n\n<a href=\"/metrs.php?id=537&type=tvorch\" class=\"redhr1\">Творчість</a> |\n<a href=\"/metrs.php?id=537&type=biogr\" class=\"redhr1\">Біографія</a> |\n<a href=\"/metrs.php?id=537&type=critiques\" class=\"redhr1\">Критика</a>\n\n<h4>ГЛЕК</h4>\n<!--<div style=\"float:right;margin-left: 10px\">\n\t<script async src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n\n\t<ins class=\"adsbygoogle\"\n\t\t style=\"display:inline-block;width:250px;height:400px\"\n\t\t data-ad-client=\"ca-pub-5357335372099528\"\n\t\t data-ad-slot=\"7581761695\"></ins>\n\t<script>\n\t(adsbygoogle = window.adsbygoogle || []).push({});\n\t</script>\n</div>-->\n<i>Присвячено Грацькові Основ'яненкові</i><br><br>\nЙшов Глек Петро уже пізненько добою<br>\nВід панотця та й так балакав сам з собою:<br>\n\"Ні, ні, панотче, ні! Щоб помилився<br>\nТа не в своїй я хаті опинився...<br>\nНі, ні, сього не буде вже зо мною!..<br>\nДоба, бач, пізня... Та й сею добою, -<br>\nХоч трохи, бач, і випив, сількость! - дочвалаю<br>\nТаки до хати... Вже і мріє із-за гаю!<br>\n Ось божії на небі зірочки,<br>\n Мов на великдень в церкві свічечки,<br>\n Дна на другою блись та блись,<br>\n А далі всі і зайнялись!..<br>\nА онде й молодик! Здоров був, молодче!<br>\nЕге, чи він то чує, як його хто кличе?<br>\n Заліз собі за хмару та й куня! -<br>\n Та де се я? Отсе іще пеня!<br>\n Он там щось бовваніє, мов стіжки...<br>\n Так не стіжки... е, бачу, могилки!<br>\n Се гробовище, бачу! Одне мріє<br>\n Й зелений хрест - старий там сотник тліє!<br>\nТа що то був з його колись за чоловік!<br>\nІ довго жив: такий дай, боже, й довгий вік!<br>\n В неділю, в свято спозаранку<br>\n Отсе ворота і оддзяпить:<br>\n Іди, іди, хто тільки втрапить, -<br>\n Є всякому горілочки чарка.<br>\nЙ усе! Е, вже таких, мабуть, нема на світі!<br>\nХоч так-таки перебери в усім повіті,<br>\nХоч з свічкою шукай, нема-таки, нема!<br>\nА вже ума того було в нього, ума!..<br>\n<br>\n А осьдечки й Семен Патика,<br>\n Хоч голова була й велика,<br>\nТа розуму вділив господь їй трохи:<br>\nВсе жінки, було, слуха він, Явдохи!<br>\nЧи до людей піти, чи жито жати,<br>\nЧи то другая деяка робота -<br>\nУсім Явдоха вже розпоряджає!<br>\nНу, так що вже не вийде, було, з хати,<br>\nНе тільки щоб без жінки за ворота:<br>\nВоно б то й чудно, а в людей буває!<br>\n<br>\nЕ! не такий був Опанас Куліш:<br>\nТо, бачиться, лежить він в ліву руку...<br>\nГолінний вдався на усяку штуку;<br>\nЧи віз, чи колесо, чи то леміш<br>\nПолагодить тобі або сокиру, -<br>\nДо його йди: він вже такий... хоч що утне!<br>\nЗ біди не раз визволював він і мене,<br>\n Та йшли до його із усього миру!<br>\nА його жінка ще к тому й знахарювала,<br>\nТа таки-так, що декому і помагала.<br>\n<br>\n Побіля його і Юхим Стонога:<br>\n Так нізавіщо згинув він, небога!<br>\nБула худібонька, і люди поважали;<br>\nА як на старість поховав він зятя<br>\nЙ дочку, так таки те, що від печалі,<br>\nТа й так - від чого, хто його вже знає, -<br>\nЩодня, сердега, він в шинок, було, чвалає...<br>\nНе те щоб сам він пив - він і людців частує -<br>\nТак, бач, було, вже дуже часто чимчикує!<br>\n А далі нінавіщо звівся...<br>\n Зимою якось запізнився<br>\n(Та і лиха ж була година!) -<br>\nЗакляк, сердега, в хуртовині...<br>\n<br>\nОн там, біля верби, Олекса Шпичка:<br>\nРусявий чоловік, а хата невеличка,<br>\nІ по сусідству жив - кумедний чоловік!<br>\nНу так, що в вигадках пройшов його ввесь вік...<br>\nІ видумав він на своїм віку чимало,<br>\nТа все за ним собі в могилу почвалало!<br>\nОтсе, було, сидить він в мене або в кума, -<br>\nВсі торохтять, а він тихесенько... все дума!<br>\nА там, гляди! по-своєму млин і змайструє,<br>\n\"Та се не млин!\" - з розумних дехто, було, скаже.<br>\n\"Ні, ні...\" - москаль народові, було, толкує.<br>\nА млин тобі, як дьогтем він його підмаже,<br>\nЯк піде, було, драть - і вітру мов немає.<br>\nДере він, крилами, як той орел, махає!<br>\nОтсе майстри: \"Навчи, будь ласкав!\" - та з поклоном!<br>\nА він: \"От бозна-що! Хіба воно так трудно?<br>\nПодумай лишень сам! Хіба подумать нудно?<br>\nА вже навчить не вмію! Бий хоч макогоном!\"<br>\nНе тільки те, й узла по-нашому не зв'яже,<br>\nА все по-своєму: \"На те і розум! - було каже. -<br>\nТче і павук таку, як батько, павутину!\"<br>\nТаківський був! Отже, пішов у домовину...<br>\n<br>\n В отсім кутку Лящі; їх на селі<br>\n Було не трохи; отже, всі в землі!<br>\nОдин із них... Павло... так той аж в монастир<br>\nПішов, до Києва, та й у ченці постригся:<br>\nСпасенний чоловік!.. Вже, може, й присвятився...<br>\nЯк зійдуться, було, та як почне Псалтир -<br>\n Е, вже письмо, покійник, знав він дуже!<br>\nВоно хто його зна, що він таке читає,<br>\nТак сльози котяться, й мороз всього проймає...<br>\n А він собі чита, йому й байдуже!<br>\nЯк в Київ раз пішло наших з села чимало,<br>\n З ним бачився наш Федір Покотило.<br>\n\"Не те, - казав, - вже і в ченцях, як то бувало!\"<br>\n Урем'я б то усе переробило!..<br>\n<br>\nВсі полягли! Садки, ставки, і хати,<br>\nЙ діток покинули, хто був жонатий! -<br>\nТут матінка моя й покійний батько:<br>\nОд них малим зостався я, й мене<br>\nПрийняв Михайло Квач, покійний дядько:<br>\nЙого за те бог в царстві спом'яне!<br>\nЕ, вже таких нема, вже вивелись між нами!<br>\nВін з Запорожжя був, та як, було, почне<br>\nПро старину... Вже вивелись такі між нами...<br>\nВін взяв мене, довів до розуму, женив...<br>\nБог дав, було, й діток, та в світ пустив<br>\nНенадовго... І жінку розлучив зо мною...<br>\nУп'ять я на світі зостався сиротою...\"<br>\n<br>\nТак Глек старий балакав дуже, далі тихше,<br>\nА там замовк, схилився на могилу...<br>\nДо церкви вранці йшли - до його, він не дише:<br>\n Взяли та й положили в домовину.\n\n\n<br><br>\n"
} |
{
"word": "Triskaidekaphobia",
"definitions": [
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],
"parts-of-speech": "Noun"
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{"poster":"Dokusei","date":"2015-02-22T21:03:58.409+0000","title":"clg is really good at giving their fans high hope","subforum":"[ARCHIVED] General Discussion","up_votes":5,"down_votes":1,"body":"then they smack that hope out of their fan's mines and stomp all over it","replies":[]} |
{
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"description": "The BIP crew have been entertaining the crowd at Dore Alley Street Fair and Brian Bonds is the center of attention. He's bound to the cross with his balls tied up as the doms carry him through the crowd. After an intense flogging scene to get the crowd going, Brian is suspended on a fence with a zipper going down his thighs. The crowd cheers him on as Brian takes a shock from the cattle prod before the zipper is ripped off his legs. The BIP crew takes the naked stud back to a sex shop to have their way with him. Brian's hands are bound with clamps on his nipples as the guys play with his cock, edging him as he begins to pre-cum. When Brian begs the guys to let him cum, he gets a heavy beating instead as Hayden Richards flogs him from front to back. Brian screams for mercy so the guys drag him to the back arcade, shoving their feet and cocks through the glory holes for Brian to swallow. Hayden gets so horny he stands the boy up and bends him over, ramming his big cock up Brian's tight hole as everyone watches. The guys have Brian crawl back to the front of the store where they gangbang him one more time before showering his face with cum. Brian finally blows his load onto Hayden's boot and licks up every last drop before the guys take him back out onto the streets.",
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{
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"release_date": "2013-09-20",
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"tags": [
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"title": "Cock hungry whore cattle prodded and fucked at Dore Alley Street Fair"
} |
{
"directions": [
"Slice zucchini into 1/4 inch slices. Toss in a bowl with Italian dressing.",
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],
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],
"language": "en-US",
"source": "allrecipes.com",
"tags": [],
"title": "Grilled Zucchini II",
"url": "http://allrecipes.com/recipe/19921/grilled-zucchini-ii/"
}
|
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{"poster":"ia kien fue","date":"2017-12-05T01:34:28.109+0000","title":"¿Cuál fue tu vicio antes de League of Legends?","subforum":"Off-topic y temas personales","embed":{"description":"Happy anniversary Final Fantasy VIII! ^_^ Aspect ratio 16:9 Enhanced video quality Best video quality on you tube!","url":"https://www.youtube.com/watch?v=k9IkmZLFkFw","image":"https://i.ytimg.com/vi/k9IkmZLFkFw/hqdefault.jpg"},"up_votes":8,"down_votes":0,"body":"Pregunta simple, ¿qué juegos eran los que te viciaban días enteros? Esos que te hacían olvidar ir a comer en las horas adecuadas o hacían enojar a tu mamá por no ir a comprar el pan.\r\n\r\nEn mi caso, soy de la era del PSX y PS2, y como saben, era prácticamente sacrílego no tener un Final Fantasy en ellas, siempre se hablaba de que llevaban al límite los gráficos, que la música era a otro nivel, que las historias y los personajes eran algo que solo esa saga era capaz de narrar tan bien, etcétera. \r\nLa primera vez que probé uno de ellos, fue cuando me compraron el 8 pirata, lo recuerdo bien, porque en la portada salía la foto del que lo pirateó,{{sticker:zombie-brand-facepalm}} y quedé loco con la secuencia de inicio, con los personajes principales peleando espada a espada y la música orquestada de fondo; en ese entonces tenía como 7 años y aunque no entendía mucho la historia, ver las animaciones de las batallas eran lo suficientemente satisfactorias como para llegar directo a la casa, después del colegio, prender la tele, esperar a que se prendiera el Play (y reconociera el CD pirata, que a veces no funcionaba como hasta el tercer intento) y quedarme ahí hasta que me echaran, porque querían ver las noticias de la noche.\r\n\r\nHace poco me compré el 15 y lo estoy disfrutando igual que cada uno de los anteriores.","replies":[{"poster":"Seratine","date":"2017-12-05T02:05:46.068+0000","up_votes":6,"down_votes":0,"body":"Los jueguitos en el celu, en especial los de gameloft :v\nAntes no tenia compu y el celular era lo único que me entretenía :´v","replies":[{"poster":"ia kien fue","date":"2017-12-05T02:11:08.317+0000","up_votes":2,"down_votes":0,"body":"JAJAJAJAJA, los que eran copias del gta {{sticker:zombie-brand-mindblown}}","replies":[]}]},{"poster":"Kłaytøn","date":"2017-12-05T02:51:52.517+0000","up_votes":5,"down_votes":1,"body":"El 1.6 aguante el 1.6 toda la vida papa!!!","replies":[]},{"poster":"Mazer Rakham","date":"2017-12-05T03:53:05.523+0000","up_votes":1,"down_votes":0,"body":"Si nos vamos a ir taaaaaaaaaaan atrás en el tiempo... Imaginate que tuve el Family, la NES y el Sega Génesis xD. Qué tiempos los del Circus Charlie y el juego de Captain Tsubasa en japones! (no entiendía ni jota, lógicamente xD) Apareció la primer PC en lo de mis tíos y fue la perdición! Todos los juegos que conseguías en un CD de ROMS y nunca habías podido jugar en un sólo CD. Con el tiempo conocí los cyber y bueno, ahí nos rateabamos del colegio para ir a jugar al CS, al Lineage, al Ragnarok o al Diablo 2 xD Aparte, enfrente del colegio teníamos un localcito que tenía metegol y un par de arcades, así que mientras esperabamos para entrar al taller o E. F. nos la pasabamos ahí. Luego pude tener mi pc y bueeeeeeeeeno, convengamos que me pasé mis buenas horas jugando al Warcraft, Secret of the Soltice, Metin 2, etc. Jugé LoL un tiempo, luego lo dejé y volví hace unos 4 años. En ese tiempo me envicié con la Play 3 y la Wii, más tarde la 3DS. Lo último que jugué o juego aú en la Pc aparte de LoL es:\n\nOri and the Blind Forest (hermoso ♥)\nSouthpark: The stick of the truth\nDivinity 2 (lo estoy jugando ahora)\nSword Coast Legends\nDiablo 3 y su expanción junto con la nueva clase.\nZombie Playground \n\nYyyyy.... probablemente me olvide de alguno más xD","replies":[{"poster":"rokushoger","date":"2017-12-05T11:56:32.100+0000","up_votes":3,"down_votes":0,"body":"> Ori and the Blind Forest\n\nPensé que era el único que conocía este juego. Visualmente es una maravilla, y de jugabilidad también.","replies":[{"poster":"Koto","date":"2017-12-05T18:54:39.354+0000","up_votes":1,"down_votes":0,"body":"Lo conoce todo el mundo xd","replies":[]},{"poster":"Mazer Rakham","date":"2017-12-05T16:48:26.490+0000","up_votes":1,"down_votes":0,"body":"Es precioso!! Lo amé desde el primer momento ♥","replies":[]}]},{"poster":"Tomas3D","date":"2017-12-05T09:18:09.477+0000","up_votes":2,"down_votes":0,"body":"Jaja que tiempos esos,yo tenia el family y jugaba un juegazo(en esos tiempos) que se llamaba archy 1000 juegos que tenia de todo.{{sticker:sg-lulu}}","replies":[{"poster":"Mazer Rakham","date":"2017-12-05T16:57:28.181+0000","up_votes":1,"down_votes":0,"body":"Era lo más el family! (lo más cuadrado también xD)\n\nYo me enviciaba con el Circus Charlie y nunca pude pasar el nivel de los monitos que te venían 3 juntos\n\n{{sticker:sg-janna}} \n\nLos cartuchos esos de no-se-cuantos juegos en uno eran geniales! (después te pasabas un rato largo soplándolos porque no arrancaban xD)","replies":[{"poster":"Tomas3D","date":"2017-12-05T17:42:45.034+0000","up_votes":1,"down_votes":0,"body":"Mal jajaja ensima despues me entere que soplarlos los rompia xD","replies":[{"poster":"Mazer Rakham","date":"2017-12-05T17:57:48.322+0000","up_votes":1,"down_votes":0,"body":"Eran mitos, como el de reiniciar el Family 5 veces y esas cosas. Funcionaban de pedo, no porque realmente fueran útiles xD","replies":[{"poster":"Tomas3D","date":"2017-12-05T18:22:15.665+0000","up_votes":2,"down_votes":0,"body":"Mal jajaja,le pasa algo \"Resetea,resetea\"","replies":[]}]}]}]}]},{"poster":"ia kien fue","date":"2017-12-05T06:00:36.789+0000","up_votes":1,"down_votes":0,"body":"Tremendo veterano de guerra, yo tuve un tiempo la super famicom y jugaba al Earthbound y el FFVI. El primer PC que tuvimos en mi casa era de los que todavía traían floppy discs (disquete), cuando tenía como 4 años mi hermano mayor trajo el Doom 2 y lo jugábamos a escondidas porque sino nos retaban por toda la sangre, jajaja.","replies":[{"poster":"Mazer Rakham","date":"2017-12-05T16:52:52.493+0000","up_votes":1,"down_votes":0,"body":"Jaja y... calcula que ya estoy entrando en los 30 xD \n\nImaginate que a mi no me dejaban ver Los Simpsons. Jugabamos al Mortal Kombat porque mi primo lo tenía y era mayor (y no estaba mi abuela para retarnos xD)","replies":[]},{"poster":"Tortuguita","date":"2017-12-05T06:40:44.473+0000","up_votes":1,"down_votes":0,"body":"Las palabrotas del mortero o de los soldados rasos en warcraft 3 me hacian que me comiera unos gritos xD JAJAJA","replies":[]}]}]},{"poster":"Maiev Shadowsong","date":"2017-12-05T06:13:14.507+0000","up_votes":3,"down_votes":0,"body":"Warcraft III: Reign of Chaos y warcraft 3 frozen throne esos era una belleza {{sticker:sg-ahri-2}}","replies":[{"poster":"rokushoger","date":"2017-12-05T11:53:41.394+0000","up_votes":1,"down_votes":0,"body":"Usted es de la vieja escuela como yo! Que buen gusto.","replies":[]},{"poster":"ia kien fue","date":"2017-12-05T06:33:25.103+0000","up_votes":1,"down_votes":0,"body":"Esos eran los clásicos del cyber, a veces ni siquiera jugaba, sino que pasaba a ver a los que eran los más capos {{sticker:slayer-pantheon-popcorn}}","replies":[]}]},{"poster":"iTargaryen","date":"2017-12-05T14:36:35.495+0000","up_votes":2,"down_votes":0,"body":"ImperiumAO, un juegazo de hace años que lo arruinaron hace no mucho tiempo.\n\nSolo entenderán los que alguna vez tuvieron la suerte (para bien o para mal) de jugar al Argentum o similares y caer en su vicio jajaj.","replies":[]},{"poster":"Mioen","date":"2017-12-05T02:36:02.825+0000","up_votes":1,"down_votes":0,"body":"http://i.imgur.com/XqBbH10.gif?noredirect\n\nMe encanta la saga metal slug, he jugado una burrada de horas a este juego. Desde emulador hasta en consola, siempre le dedique mis horas a este juego(Bastante considerables).\n\nLo jugué tanto con teclado, mando y arcade(En arcade no tuve tanta suerte ya que murieron y no estuve tanto en esa época.)\n\nPor otra parte en juegos online. Rakion.\n\nhttps://st-listas.20minutos.es/images/2016-02/407337/4903992_640px.jpg?1455156446\n\nComencé como Warrior, luego Blacksmith, y por último Archer (Y no me arrepiento)\n\nJugar como Archer era para sacarle su madre a las demás clases, ya que su Special era bastante fuerte. Luego el juego se fue a la mierda ya que he intento ser un P2W. Y tras 10 años intentaron crearon un nuevo PJ, pasando de 5 a 6. También querían quitar el factor P2W, pero como la cagaron feo ahora ser nuevo significa ser violado por jugadores que llevan más de 4 años.\nY yo que pensaba no volver más, cada tanto me doy una vuelta.(Solo que ahora eliminé mis set +14 Mejorados(Por que mi set Bolívar y mi set Noblesse T.T)\nAhora solo tengo un triste set Cane.)","replies":[{"poster":"ia kien fue","date":"2017-12-05T06:05:30.623+0000","up_votes":2,"down_votes":0,"body":"Uff... Cómo olvidar los Metal Slug, cerca de la casa donde viví hasta los 13 había un local con máquinas arcades, una en especial tenía todo el catálogo del Neo Geo y pasaba jugando Metal Slug y King of Fighters 2002 (en un momento me hice tan bueno jugando, que la señora del local decía que me tenía que ir porque llevaba mucho rato sin perder y pagar por otras fichas)","replies":[]}]},{"poster":"dscpnak","date":"2017-12-05T01:52:05.070+0000","up_votes":1,"down_votes":0,"body":"si digo call of duty mw2 quedo como una ratilla no? {{champion:29}}","replies":[{"poster":"ia kien fue","date":"2017-12-05T01:53:11.865+0000","up_votes":2,"down_votes":0,"body":"No vieja! Siempre digno, hasta yo admito que el Black Ops me quedó gustando {{sticker:sg-ezreal}}","replies":[{"poster":"dscpnak","date":"2017-12-07T02:30:18.210+0000","up_votes":1,"down_votes":0,"body":"tome su laik buen hombre cod fue hasta el black ops despues todo en picada (leve remontada en black ops 3 pero por ser de treyarch nadamas)","replies":[]}]}]},{"poster":"Tortuguita","date":"2017-12-05T04:10:55.776+0000","up_votes":2,"down_votes":1,"body":"Madre mía el Half life , me lo pase como 8 veces a cada uno de sus capítulos. Luego vino el Company of Heroes junto con el Dawn of war 2 los cuales los sigo jugando hasta ahora aun mas que el lol. Son unos clasicasos y cada partida tiene un evento diferente , todavía recuerdo una misión en Calderis contra los Tiranidos (bichos como aliens) en la cual mi grupo de marines tácticos empezó a avanzar en la niebla de guerra y no muy cerca de la escalera se escucharon los \"pum - pum\" de un carnifex. Milisegundos tuve para reaccionar ante la terrible abominación (la primera que había visto) y acto seguido a pesar de mi rapidez empala a un Marine con sus patas delanteras en forma de aguja , lo mastica y luego lo lanza contra la pared destruyendola y cayendo su cuerpo entre los mancillados escombros. Terriblemente ÉPICO ! (Lo fundimos a fuego de bolter y lanzamisiles , la mision me duro 35 min luego de esa pesadilla xD)","replies":[{"poster":"ia kien fue","date":"2017-12-05T06:07:02.625+0000","up_votes":1,"down_votes":0,"body":"¿Te jugaste las expansiones del HL1? El opposing force lo encontré incluso mejor que la campaña del HL1 normal {{sticker:zombie-nunu-hearts}}","replies":[{"poster":"Tortuguita","date":"2017-12-05T06:39:04.359+0000","up_votes":1,"down_votes":0,"body":"El opposing force es mucho mejor en temas de acción y fluidez , casi en ningún momento te puedes aburrir o simplemente pararte a pensar , lo que le da la verdad sensación que tiene un marine en medio de una cascada de resonancia , CAOS. En cambio half life es mas pensativo y sensato en el lugar y momento en donde poner al jugador , lo excelente es como lo guía a través de los escenarios los cuales son mas dinámicos que los de opposing force. Blue shift es meh , pero es jugable también. :3","replies":[]}]}]},{"poster":"KillgoreZ","date":"2017-12-05T03:01:43.311+0000","up_votes":1,"down_votes":0,"body":"Antes del lol viciaba al overwatch (ya c que soy nuevo >:v) pero mi mayor viciada fue con el pvz <3 una vez jugué un dia completo.","replies":[{"poster":"ia kien fue","date":"2017-12-05T06:02:22.364+0000","up_votes":1,"down_votes":0,"body":"Jugaría OW solo para usar a D.VA, por, bueno, razones... {{summoner:11}}","replies":[{"poster":"KillgoreZ","date":"2017-12-06T21:56:21.278+0000","up_votes":1,"down_votes":0,"body":"Aguante junkrat xD","replies":[]}]}]},{"poster":"Rebkans","date":"2017-12-05T04:54:59.306+0000","up_votes":1,"down_votes":0,"body":"A mi me encantaba el gta 2 de la ps1.\nHasta el dia de hoy me sigue causando mucha gracia la inteligencia artificial que tiene ese juego, como cuando te perseguia la policia y ellos terminaban matando mas gente que uno mismo o cuando se llevaban por delante a sus propios compañeros.\n\nEl call of duty 2 y battlefield 2 eran una droga tambien.","replies":[{"poster":"ia kien fue","date":"2017-12-05T05:53:55.431+0000","up_votes":1,"down_votes":0,"body":"A mi me mareaba la vista desde arriba de los dos primeros gta, jajaja. El COD 2 fue buenísimo, no creo que haya juego basado en la segunda guerra mundial que le haga pelea, al menos en los fps.","replies":[{"poster":"Rebkans","date":"2017-12-06T18:53:21.009+0000","up_votes":1,"down_votes":0,"body":"Jajajaj si, es cierto. Yo tambien me mareaba de vez en cuando, especialmente en las zonas 2 y 3 que eran en las que menos jugaba.\n\n> El COD 2 fue buenísimo, no creo que haya juego basado en la segunda guerra mundial que le haga pelea, al menos en los fps.\n\nSee, ese juego es un ejemplo de que los graficos no son lo mas importantes. Ademas, la banda sonora es excelente. El otro dia me puse a escuchar algunos temas y me dio tremenda nostalgia xD\n\nHace relativamente poco conecté la play 2 y me puse a jugar al metal slug, otro juegazo aunque la consola mas vieja que tuve, fue el family y despues el sega, ps1 y ps2\nRecuerdo que jugaba al mario bross ( del familly) pero nunca superaba el nivel 3 por lo manco que era xD.","replies":[]}]}]},{"poster":"Tio Davy Jones","date":"2017-12-06T06:52:12.746+0000","up_votes":1,"down_votes":0,"body":"El mio fue el dota 2, llegue a tener 1100hs y termine pasándome al lol por culpa de mis amigos y mi hermano, nunca mas pude jugar mas de 1 partida en dota 2, cada tanto lo actualizo pero no me alcanzan las ganas para buscar una partida","replies":[]},{"poster":"Koto","date":"2017-12-05T18:54:09.265+0000","up_votes":1,"down_votes":0,"body":"Cs 1.6. obviamente no steam (?","replies":[]},{"poster":"Soviet Yunyun","date":"2017-12-05T17:13:29.491+0000","up_votes":1,"down_votes":0,"body":"perfect world un mmorpg, me llegaba a tirar 12 hrs al dia jugando XD","replies":[]},{"poster":"KøDï","date":"2017-12-05T16:50:59.496+0000","up_votes":1,"down_votes":0,"body":"El cs 1.6 que tiempos esos, nunca me olvidare de ese juegaso.","replies":[]},{"poster":"Elardon","date":"2017-12-05T03:17:30.283+0000","up_votes":1,"down_votes":0,"body":"Antes de jugar al lol jugaba al CS 1.6, y al Argentum Online, en especial el Tierras Perdidas, este me sacó tantas horas como el lol (creo que más), y antes de tener PC era todo Mortal Kombat con el sega entre otros juegos. Tambien la Psx y la Ps2 (de estas dos la que mas me viciaba era la psx, no se porque pero habia algo que no me gustaban de los juegos de estas consolas que no me llamaba la atencion o no me parecian tan viciables) con el Metal Gear Solid y MK.","replies":[{"poster":"Láwliet","date":"2017-12-05T13:17:45.661+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=Elardon,realm=LAS,application-id=J8qpiO2I,discussion-id=ahK4T1MM,comment-id=000b,timestamp=2017-12-05T03:17:30.283+0000)\n>\n> Antes de jugar al lol jugaba al CS 1.6, y al Argentum Online, en especial el Tierras Perdidas, este me sacó tantas horas como el lol (creo que más), y antes de tener PC era todo Mortal Kombat con el sega entre otros juegos. Tambien la Psx y la Ps2 (de estas dos la que mas me viciaba era la psx, no se porque pero habia algo que no me gustaban de los juegos de estas consolas que no me llamaba la atencion o no me parecian tan viciables) con el Metal Gear Solid y MK.\n\nQue loco, yo también jugaba al Tierras Perdidas jaja.","replies":[{"poster":"iTargaryen","date":"2017-12-05T15:00:31.430+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=Láwliet,realm=LAS,application-id=J8qpiO2I,discussion-id=ahK4T1MM,comment-id=000b0000,timestamp=2017-12-05T13:17:45.661+0000)\n>\n> Que loco, yo también jugaba al Tierras Perdidas jaja.\n\nsomos 3 jaja","replies":[]}]}]},{"poster":"Hísøka","date":"2017-12-05T13:09:48.238+0000","up_votes":1,"down_votes":0,"body":"Last Chaos como por 6 o 7 años. \n{{sticker:sg-jinx}}","replies":[]},{"poster":"Bienzahar","date":"2017-12-05T12:52:30.463+0000","up_votes":1,"down_votes":0,"body":"LA saga de Far Cry xD","replies":[]},{"poster":"SquidCrazy","date":"2017-12-05T12:49:57.397+0000","up_votes":1,"down_votes":0,"body":"El timmesplitter future perferct para la play 2 con ese juego me quedaba toda la noche jugando sin parar, que recuerdos...","replies":[]},{"poster":"rokushoger","date":"2017-12-05T11:52:26.121+0000","up_votes":1,"down_votes":0,"body":"Warcraft 3 frozen throne, y su correspondiente mapita dota. Además me encantaba el Crash team racing de play 1 (gané torneos varios de eso a nivel regional) y me siguen encantando los juegos de RPG. El mejor juego que jugué de ese estilo se debate entre el grandia 3, el breath of fire IV y el Final Fantasy VIII.","replies":[]},{"poster":"Bae Joo Hyun","date":"2017-12-05T10:57:43.490+0000","up_votes":1,"down_votes":0,"body":"AoE 2, CS 1.6 y M&B: Warband","replies":[]},{"poster":"MASITAS","date":"2017-12-05T10:50:55.944+0000","up_votes":1,"down_votes":0,"body":"cs 1.6 y go. El go sigue siendo","replies":[]},{"poster":"XenutPvP","date":"2017-12-05T10:33:06.731+0000","up_votes":1,"down_votes":0,"body":"Un juego del hombre araña en la ps2 que no me puedo acordar el nombre, desde el mediodia hasta la noche viciando, era buenisimo.","replies":[]},{"poster":"Tomas3D","date":"2017-12-05T09:16:06.098+0000","up_votes":1,"down_votes":0,"body":"Uno que es de celular de Gameloft que se llama Modern Combat 5 y el Asphalt 8,bueno la cosa es que yo tengo Windows 8 y esos juegos tienen una versión para pc(nose si esta en Windows 10) y bueno ahi me vicie duro con esos juegos,antes de eso jugaba al Clash of clans y en la play 2 al pes mas que nada.\n\nPero el LOL es el mejor juego que juge.","replies":[]},{"poster":"HL3 Confirmed","date":"2017-12-05T08:47:06.129+0000","up_votes":1,"down_votes":0,"body":"el cs 1.6 o el killing floor 1, todo el dia toda la noche","replies":[]},{"poster":"Icesoul","date":"2017-12-05T04:30:05.965+0000","up_votes":1,"down_votes":0,"body":"mmm los juegos de pokemon, sigo jugandolos igual, los de nintendo, nada online, jugaba uno de digimon tambien, ese era online era de la tematica de digimon 3, tamers, jugaba tambien al Heroes Mu recuerdo, era una webada ese pero me gustaba vender cosas y regalar, pero vicio mas mas mas mas grande que tuve fue el resident evil 4, me lo habre pasado como 30 veces en diferentes modos, con solo un cuchillo y una pistolita, con solo lanza cohetes, etc, etc, siempre hallaba nuevas formas y retos para darmelo vuelta, matar a todos dejando el mapa super limpito, en el menor tiempo, alto problema tuve con ese juego jaja, y sabes que? lo volveria a jugar xD","replies":[]}]} |
{"category": "Civil Appeal", "status": "Disposition Filed/Case Closed", "case_url": "http://caseinfo.nvsupremecourt.us/public/caseView.do?csIID=8266", "caption": "LAWSON VS. TROY & NICHOLS, INC.", "type": "General", "case_no": "40415", "subtype": "Other", "parties": [{"Represented By": "Janalee M. Murray", "Role": "Appellant", "Party Name": "Lela M. Lawson"}, {"Represented By": "Janalee M. Murray", "Role": "Appellant", "Party Name": "Terry Lawson"}, {"Represented By": "Darrell Lincoln Clark", "Role": "Respondent", "Party Name": "Charles Holly"}, {"Represented By": "Darrell Lincoln Clark", "Role": "Respondent", "Party Name": "Charles Holly Money Purchase Pension Plan"}, {"Represented By": "NA", "Role": "Respondent", "Party Name": "Chase Manhattan Mortgage Corporation"}, {"Represented By": "Kent F. Larsen (Smith Larsen & Wixom)", "Role": "Respondent", "Party Name": "Troy & Nichols, Inc."}], "docket": [{"Date": "10/30/2002", "Type/Subtype": "Filing Fee - Filing Fee Paid", "Description": "Received Filing Fee Paid on Filing. $200.00 from Janalee M. Murray, Esq.--check no. 2855."}, {"Type/Subtype": "Notice of Appeal Documents - Certified Copy of Notice of Appeal/Settlement", "Description": "Filed Certified Copy of Notice of Appeal/Settlement. Notice re: settlement conference program/suspension of rules mailed to all counsel. (Docketing statement mailed to counsel for appellant.)", "Document Number": "02-18698", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=77113&sireDocumentNumber=02-18698", "Date": "10/30/2002", "Pending?": "NA"}, {"Date": "10/30/2002", "Type/Subtype": "Notice/Outgoing - Notice to File Case Appeal Statement/Civil", "Description": "Issued Notice to File Case Appeal Statement. Due Date: 10 days"}, {"Date": "10/31/2002", "Type/Subtype": "Notice/Outgoing - Notice to Transmit Required Document", "Description": "Issued Notice to Transmit Required Document. Four orders of dismissal filed on 10/14/02. Due Date: 10 days"}, {"Date": "11/06/2002", "Type/Subtype": "Settlement Notice - Notice: Assignment to Settlement Program", "Description": "Issued Notice: Assignment to Settlement Program. Settlement Judge: Lester H. Berkson. (Briefing and preparation of transcripts suspended pending further order of this court.)"}, {"Type/Subtype": "Order/Incoming - District Court Order", "Description": "Filed District Court Order. Certified copy of Stipulation and Order to Dismiss Crossclaim filed in district court on October 14, 2002.", "Document Number": "02-19297", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=77382&sireDocumentNumber=02-19297", "Date": "11/08/2002", "Pending?": "NA"}, {"Type/Subtype": "Notice/Incoming - Notice of Change of Address", "Description": "Filed Notice of Change of Address. Notice of Counsel Change of Address and Fax.", "Document Number": "02-19395", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=77473&sireDocumentNumber=02-19395", "Date": "11/12/2002", "Pending?": "NA"}, {"Date": "11/22/2002", "Type/Subtype": "Docketing Statement - Docketing Statement", "Description": "Received Docketing Statement. Called for a motion to file late. Due date: 10 days."}, {"Type/Subtype": "Order/Procedural - Order", "Description": "Filed Order. On October 30, 2002, this court issued a notice directing appellants to file a case appeal statement. To date, appellants have failed to comply with this court's notice. Appellants shall, within 10 days from the date of this order, file a case appeal statement in the district court and file, with this court, two certified copies of the case appeal statement.", "Document Number": "02-20645", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=185519&sireDocumentNumber=02-20645", "Date": "12/03/2002", "Pending?": "NA"}, {"Type/Subtype": "Notice of Appeal Documents - Case Appeal Statement", "Description": "Filed Case Appeal Statement. Certified copy filed in district court on: November 1, 2002.", "Document Number": "02-21017", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=78705&sireDocumentNumber=02-21017", "Date": "12/09/2002", "Pending?": "NA"}, {"Date": "12/12/2002", "Type/Subtype": "Order/Incoming - District Court Order", "Description": "Received District Court Order. Received from attorney Janalee Murray a copy of Notice of Entry of Judgment filed in district court on 12/5/02 and Judgment filed 11/20/02. (No action required per AH.)"}, {"Date": "12/17/2002", "Type/Subtype": "Notice/Outgoing - Letter", "Description": "Letter. Lester H. Berkson. Case Appeal Statement."}, {"Date": "12/18/2002", "Type/Subtype": "Order/Incoming - District Court Order", "Description": "Received District Court Order. Received from attorney Janalee Murray a copy of Notice of Entry of Judgment filed in district court on 9/24/02 and Judgment filed 9/20/02. (No action required.)"}, {"Date": "12/27/2002", "Type/Subtype": "Notice/Outgoing - Letter", "Description": "Letter. Lester H. Berkson. (district court order filed on 12/5/02)"}, {"Type/Subtype": "Notice/Incoming - Notice of Change of Address", "Description": "Filed Notice of Change of Address. Law firm of Smith Larsen & Wixom.", "Document Number": "03-01064", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=203794&sireDocumentNumber=03-01064", "Date": "01/21/2003", "Pending?": "NA"}, {"Type/Subtype": "Settlement Program Report - Interim Settlement Program Report", "Description": "Filed Interim Settlement Program Report. The parties have agreed to a settlement of this matter.", "Document Number": "03-01163", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=151623&sireDocumentNumber=03-01163", "Date": "01/22/2003", "Pending?": "NA"}, {"Type/Subtype": "Order/Procedural - Order", "Description": "Filed Order. The settlement judge has filed a report indicating that the parties have agreed to a settlement. Appellant shall have 30 days to file a stipulation or motion to dismiss or otherwise inform this court of the status of this appeal.", "Document Number": "03-01670", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=81085&sireDocumentNumber=03-01670", "Date": "01/30/2003", "Pending?": "NA"}, {"Type/Subtype": "Motion - Stipulation/Dismiss Appeal", "Description": "Filed Stipulation/Dismiss Appeal.", "Document Number": "03-03976", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=82905&sireDocumentNumber=03-03976", "Date": "03/10/2003", "Pending?": "NA"}, {"Type/Subtype": "Order/Clerk's - Clerk's Order Approving Stipulation", "Description": "Filed Clerk's Order Approving Stipulation. Order Partially Dismissing Appeal. Pursuant to the stipulation filed on March 10, 2003, this appeal is dismissed as to appellants and respondent Chase Manhattan Mortgage Corporation, only. NRAP 42(b). The clerk of this court shall remove Chase Manhattan Mortgage Corporation, a New Jersey Corporation, as respondent from the caption on this court's docket.", "Document Number": "03-04859", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=83574&sireDocumentNumber=03-04859", "Date": "03/21/2003", "Pending?": "NA"}, {"Type/Subtype": "Order/Procedural - Order", "Description": "Filed Order. To date, appellants have failed to file the requested motion for an extension of time to file the docketing statement. In the interest of judicial economy, we direct the clerk of this court to file the untimely docketing statement.", "Document Number": "03-05014", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=83704&sireDocumentNumber=03-05014", "Date": "03/25/2003", "Pending?": "NA"}, {"Type/Subtype": "Docketing Statement - Docketing Statement", "Description": "Filed Docketing Statement.", "Document Number": "02-20142", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=83705&sireDocumentNumber=02-20142", "Date": "03/25/2003", "Pending?": "NA"}, {"Type/Subtype": "Order/Procedural - Order", "Description": "Filed Order. On March 21, 2003, this court entered an order approving the stipulation and dismissing this appeal as to Chase Manhattan Mortgage Corpoation. To date, this court has received no information regarding the status of this apepal as to the remaining respondents. Appellants shall have 15 days to file a stipulation or motion to dismiss or inform this court of the status of this appeal. Failure to comply timely with this order may result in the dismissal of this appeal as abandoned.", "Document Number": "03-10876", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=88287&sireDocumentNumber=03-10876", "Date": "06/30/2003", "Pending?": "NA"}, {"Type/Subtype": "Letter/Incoming - Letter", "Description": "Filed Letter. from attorney Janalee M. Murray regarding the parties involved in this appeal.", "Document Number": "03-11561", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=88672&sireDocumentNumber=03-11561", "Date": "07/09/2003", "Pending?": "NA"}, {"Type/Subtype": "Order/Dispositional - Order/Voluntary Dismissal", "Description": "Filed Voluntary Dismissal. On July 9, 2003, appellants' counsel, Janalee M. Murray, submitted a letter in which she indicted \"all issues in Case No. 40415 have been resolved with the [filing of the] stipulation for dismissal [between] Chase and the Lawsons.\" The clerk of this court shall file the letter received on July 9, 2003. We elect to treat the letter as a motion for voluntary dismissal of this appeal. fn2[We remind Ms. Murray that in the future, requests for relief from this court should be presented in a formal motion, not through an informal letter.] We grant the motion and \" . . . dismiss this appeal.\" SNP03-MS/ML/NB", "Document Number": "03-13916", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=90171&sireDocumentNumber=03-13916", "Date": "08/20/2003", "Pending?": "NA"}, {"Type/Subtype": "Letter/Incoming - Letter", "Description": "Filed Letter. from attorney Janalee M. Murray regarding the parties involved in this appeal.", "Document Number": "03-11561", "Document URL": "/document/view.do?csNameID=8266&csIID=8266&deLinkID=90172&sireDocumentNumber=03-11561", "Date": "08/20/2003", "Pending?": "NA"}, {"Date": "08/20/2003", "Type/Subtype": "Case Status Update - Case Closed", "Description": "Case Closed. No remittitur issued."}], "filed.date": "10/30/2002", "metadata": {"To SP/Judge:": "11/06/2002 / Berkson, Lester", "Lower Court Case(s):": "Clark Co. - Eighth Judicial District - A423591", "Submission Date:": "NA", "Panel Assigned:": "Panel", "Case Status:": "Disposition Filed/Case Closed", "Replacement:": "NA", "Oral Argument Location:": "NA", "Classification:": "Civil Appeal - General - Other", "Oral Argument:": "NA", "Disqualifications:": "NA", "SP Status:": "Completed", "Short Caption:": "LAWSON VS. TROY & NICHOLS, INC.", "How Submitted:": "NA"}} |
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
}, {
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
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}, {
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
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"author":"张三",
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
},{
"id":2015,
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"createtime":"2017-10-11",
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"author":"张三",
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"details":"崇祯五年十二月，余住西湖。大雪三日，湖中人鸟声俱绝。是日更定矣，余拏一小舟，拥毳衣炉火，独往湖心亭看雪。雾凇沆砀，天与云与山与水，上下一白。湖上影子，惟长堤一痕、湖心亭一点、与余舟一芥，舟中人两三粒而已。——张岱· 《湖心亭看雪》"
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{"poster":"WhiteCl0ud","date":"2015-07-01T20:09:14.111+0000","title":"Platin Spieler such fähiges Team","subforum":"Clans & Teams","up_votes":0,"down_votes":0,"body":"In game: WhiteCl0ud\r\nDerzeit Platin 2 - 3\r\nSuche Leute bzw Team zum Ranked zocken.\r\nBin main ADC/MID\r\nBitte einfach adden ingame","replies":[]} |
{"response": {"status": "ok", "userTier": "developer", "total": 1, "content": {"id": "business/2018/may/29/drones-predicted-to-give-british-economy-a-42bn-lift-by-2030", "type": "article", "sectionId": "business", "sectionName": "Business", "webPublicationDate": "2018-05-28T23:01:08Z", "webTitle": "Drones predicted to give British economy a \u00a342bn lift by 2030", "webUrl": "https://www.theguardian.com/business/2018/may/29/drones-predicted-to-give-british-economy-a-42bn-lift-by-2030", "apiUrl": "https://content.guardianapis.com/business/2018/may/29/drones-predicted-to-give-british-economy-a-42bn-lift-by-2030", "fields": {"headline": "Drones predicted to give British economy a \u00a342bn lift by 2030", "bodyText": "Using drones to transform working practices could boost Britain\u2019s economy by \u00a342bn by 2030, research claims. Increased use of drones, in sectors from construction or defence to energy or logistics, will employ hundreds of thousands of people and lift GDP by almost 2%, according to a report by accountants PwC. While pilots and police have often viewed drones as a problem, the report predicts 76,000 unmanned aerial vehicles will be in UK skies by the end of the next decade for commercial or public use, saving billions in efficiencies. It says many existing jobs will go, with drones able to quickly map, inspect or transport in places that are difficult for people to reach. Drones are likely to replace posts from stock controllers to helicopter pilots as they allow speedier visual access of everything from giant warehouses to power lines. Already used widely in utilities to inspect infrastructure, drones are likely to replace expensive helicopter use in land surveys, the report says, while oil rig inspections have become safer and cheaper using the devices. Remote crop spraying could extend in agriculture to drone monitoring with thermal cameras, to give more data on plant health and irrigation than is visible to the human eye. PwC predicts cost savings of \u00a316bn annually through their use and estimates that in the long run there will be 628,000 people working in the drone economy, potentially in more highly skilled jobs overall, including building and programming the devices. Elaine Whyte, of PwC, said: \u201cDrones have the potential to offer a powerful new perspective for businesses across a variety of industries, delivering both productivity benefits and increased value from the data they collect. \u201cI envisage that the advantages of drone technology will be well established within the decade \u2013 not only for business purposes but also for helping to protect our society, for example, through being used by the emergency services. There is a need for current UK drone regulation to advance to see the estimations in our report become a reality but it\u2019s positive to see the government already taking proactive steps to address this with the draft drones bill.\u201d The draft bill was due to be published this spring. The government said it would pave the way for drone use in business or public service but also help police crack down on their misuse after concerns about privacy and safety, particularly for planes. Last week pilots and air traffic controllers issued new guidelines on drones to their members in response to a growing risk that they said regulation was so far failing to address. Many thousands have been sold for leisure users and pilots have reported dozens of near collisions around airports. In 2017 there were 92 official reports of incidents involving drones and planes, with some near misses on large passenger jets, which the pilots union Balpa said had the potential to cause catastrophic accidents. Whyte conceded that drones were still often seen as toys, adding: \u201cThe immediate focus must be on developing society\u2019s confidence in the technology to help drive acceptance and increase adoption. There is a huge opportunity to help solve some of business and society\u2019s most important problems.\u201d The aviation minister Baroness Sugg said drones would bring significant economic benefits and the government was attempting to harness their potential through its industrial strategy. She added: \u201cThey are already improving people\u2019s lives \u2013 helping the emergency services and keeping key national infrastructure like rail lines and power stations safe. Excitingly this is just the beginning.\u201d"}, "isHosted": false, "pillarId": "pillar/news", "pillarName": "News"}}} |
{"notes": [{"tddate": null, "ddate": null, "tmdate": 1518730191544, "tcdate": 1508451337932, "number": 21, "cdate": 1518730191535, "id": "SkffVjUaW", "invitation": "ICLR.cc/2018/Conference/-/Blind_Submission", "forum": "SkffVjUaW", "original": "B1-MViLpb", "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "writers": ["ICLR.cc/2018/Conference"], "content": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "nonreaders": [], "details": {"replyCount": 11, "writable": false, "overwriting": [], "revisions": true, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1506717071958, "id": "ICLR.cc/2018/Conference/-/Blind_Submission", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["ICLR.cc/2018/Conference"], "reply": {"forum": null, "replyto": null, "writers": {"values": ["ICLR.cc/2018/Conference"]}, "signatures": {"description": "How your identity will be displayed with the above content.", "values": ["ICLR.cc/2018/Conference"]}, "readers": {"description": "The users who will be allowed to read the above content.", "values": ["everyone"]}, "content": {"authors": {"required": false, "order": 1, "values-regex": ".*", "description": "Comma separated list of author names, as they appear in the paper."}, "authorids": {"required": false, "order": 2, "values-regex": ".*", "description": "Comma separated list of author email addresses, in the same order as above."}}}, "nonreaders": [], "noninvitees": [], "cdate": 1506717071958}}, "tauthor": "ICLR.cc/2018/Conference"}, {"tddate": null, "ddate": null, "original": null, "tmdate": 1517260076124, "tcdate": 1517250220314, "number": 868, "cdate": 1517250220299, "id": "rkV28JaHG", "invitation": "ICLR.cc/2018/Conference/-/Acceptance_Decision", "forum": "SkffVjUaW", "replyto": "SkffVjUaW", "signatures": ["ICLR.cc/2018/Conference/Program_Chairs"], "readers": ["everyone"], "writers": ["ICLR.cc/2018/Conference/Program_Chairs"], "content": {"decision": "Reject", "title": "ICLR 2018 Conference Acceptance Decision", "comment": "Regarding clarity, while the paper definitely needs work if it is to be resubmitted to an ML venue, different revisions would be appropriate for a physics audience. 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The work formulates a measure of self-resemblance, to determine when to stop increasing the feature dimensionality at each convolutional layer. The experimental section evaluates this method on MNIST, CIFAR-10/100 and a limited evaluation of ImageNet. Generally, I am a very big proponent of structure learning in neural networks. In particular, we have seen a tremendous boost in performance in going from feature engineering to feature learning, and thus can expect similar effects while learning architectures rather than manually designing them. One important work in this area is \"Self-informed neural network structure learning\" by Farley et al. that is missing from the citations. \nHowever, this work falls short of its promises.\n\n1. The title is misleading. There really isn't much discussion about the architecture of networks, but rather the dimensionality of the feature maps. These are very different concepts.\n2. Novelty of this work is also limited, as the authors acknowledge, that much of the motivation is borrowed from Hao et al., while only the expansion mechanism is now normalized to avoid rescaling issues and threshold tuning.\n3. The general approach lacks global context. All decisions about individual feature depths are made locally both temporally and spatially. In particular, expanding the feature depth at layer f at time t, may have non trivial effect on layer f-1 at time t + 1. In other words, there must be some global state-space manifold to help make decisions globally. This resembles classical dynamic programming paradigms. Local decisions aren't always globally optimal.\n4. Rather than making decision on per layer basis at each iteration, one should wait for the model to converge, and then determine what is useful and what is not.\n5. Finally, the results are NOT promising. In table 1, although the final error has reduced in most cases, it comes at the expense of increases capacity, in extreme cases as much as ~5x, and always at the increased training time, in the extreme case ~14x, An omitted citation of \"Going deeper with Convolution\" is an example, where a much smaller footprint leads to a higher performance, further underlying the importance of a smaller footprint network as stated in the abstract.\n\n", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"}, "writers": [], "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "duedate": 1511845199000, "tmdate": 1515642409425, "id": "ICLR.cc/2018/Conference/-/Paper21/Official_Review", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["ICLR.cc/2018/Conference/Paper21/Reviewers"], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/AnonReviewer1", "ICLR.cc/2018/Conference/Paper21/AnonReviewer3", "ICLR.cc/2018/Conference/Paper21/AnonReviewer2"], "reply": {"forum": "SkffVjUaW", "replyto": "SkffVjUaW", "writers": {"values": []}, "signatures": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "values": ["everyone"]}, "content": {"title": {"required": true, "order": 1, "description": "Brief summary of your review.", "value-regex": ".{0,500}"}, "review": {"required": true, "order": 2, "description": "Please provide an evaluation of the quality, clarity, originality and significance of this work, including a list of its pros and cons.", "value-regex": "[\\S\\s]{1,200000}"}, "rating": {"required": true, "order": 3, "value-dropdown": ["10: Top 5% of accepted papers, seminal paper", "9: Top 15% of accepted papers, strong accept", "8: Top 50% of accepted papers, clear accept", "7: Good paper, accept", "6: Marginally above acceptance threshold", "5: Marginally below acceptance threshold", "4: Ok but not good enough - rejection", "3: Clear rejection", "2: Strong rejection", "1: Trivial or wrong"]}, "confidence": {"required": true, "order": 4, "value-radio": ["5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature", "4: The reviewer is confident but not absolutely certain that the evaluation is correct", "3: The reviewer is fairly confident that the evaluation is correct", "2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper", "1: The reviewer's evaluation is an educated guess"]}}}, "nonreaders": [], "expdate": 1519621199000, "cdate": 1515642409425}}}, {"tddate": null, "ddate": null, "original": null, "tmdate": 1515642409485, "tcdate": 1511895672375, "number": 2, "cdate": 1511895672375, "id": "S1gFMVoeM", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Official_Review", "forum": "SkffVjUaW", "replyto": "SkffVjUaW", "signatures": ["ICLR.cc/2018/Conference/Paper21/AnonReviewer3"], "readers": ["everyone"], "content": {"title": "simple idea that is shown to work well in practice, preliminary ImageNet results demonstrate scalability", "rating": "8: Top 50% of accepted papers, clear accept", "review": "This paper introduces a simple correlation-based metric to measure whether filters in neural networks are being used effectively, as a proxy for effective capacity. The authors then introduce a greedy algorithm that expands the different layers in a neural network until the metric indicates that additional features will end up not being used effectively.\n\nThe application of this algorithm is shown to lead to architectures that differ substantially from hand-designed models with the same number of layers: most of the parameters end up in intermediate layers, with fewer parameters in earlier and later layers. This indicates that common heuristics to divide capacity over the layers of a network are suboptimal, as they tend to put most parameters in later layers. It's also nice that simpler tasks yield smaller models (e.g. MNIST vs. CIFAR in figure 3).\n\nThe experimental section is comprehensive and the results are convincing. I especially appreciate the detailed analysis of the results (figure 3 is great). Although most experiments were conducted on the classic benchmark datasets of MNIST, CIFAR-10 and CIFAR-100, the paper also includes some promising preliminary results on ImageNet, which nicely demonstrates that the technique scales to more practical problems as well. That said, it would be nice to demonstrate that the algorithm also works for other tasks than image classification.\n\nI also like the alternative perspective compared to pruning approaches, which most research seems to have been focused on in the past. The observation that the cross-correlation of a weight vector with its initial values is a good measure for effective filter use seems obvious in retrospect, but hindsight is 20/20 and the fact is that apparently this hasn't been tried before. It is definitely surprising that a simple method like this ends up working this well.\n\nThe fact that all parameters are reinitialised whenever any layer width changes seems odd at first, but I think it is sufficiently justified. It would be nice to see some comparison experiments as well though, as the intuitive thing to do would be to just keep the existing weights as they are.\n\nOther remarks:\n\nFormula (2) seems needlessly complicated because of all the additional indices. Maybe removing some of those would make things easier to parse. It would also help to mention that it is basically just a normalised cross-correlation. This is mentioned two paragraphs down, but should probably be mentioned right before the formula is given instead.\n\npage 6, section 3.1: \"it requires convergent training of a huge architecture with lots of regularization before complexity can be introduced\", I guess this should be \"reduced\" instead of \"introduced\".", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "writers": [], "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "duedate": 1511845199000, "tmdate": 1515642409425, "id": "ICLR.cc/2018/Conference/-/Paper21/Official_Review", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["ICLR.cc/2018/Conference/Paper21/Reviewers"], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/AnonReviewer1", "ICLR.cc/2018/Conference/Paper21/AnonReviewer3", "ICLR.cc/2018/Conference/Paper21/AnonReviewer2"], "reply": {"forum": "SkffVjUaW", "replyto": "SkffVjUaW", "writers": {"values": []}, "signatures": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "values": ["everyone"]}, "content": {"title": {"required": true, "order": 1, "description": "Brief summary of your review.", "value-regex": ".{0,500}"}, "review": {"required": true, "order": 2, "description": "Please provide an evaluation of the quality, clarity, originality and significance of this work, including a list of its pros and cons.", "value-regex": "[\\S\\s]{1,200000}"}, "rating": {"required": true, "order": 3, "value-dropdown": ["10: Top 5% of accepted papers, seminal paper", "9: Top 15% of accepted papers, strong accept", "8: Top 50% of accepted papers, clear accept", "7: Good paper, accept", "6: Marginally above acceptance threshold", "5: Marginally below acceptance threshold", "4: Ok but not good enough - rejection", "3: Clear rejection", "2: Strong rejection", "1: Trivial or wrong"]}, "confidence": {"required": true, "order": 4, "value-radio": ["5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature", "4: The reviewer is confident but not absolutely certain that the evaluation is correct", "3: The reviewer is fairly confident that the evaluation is correct", "2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper", "1: The reviewer's evaluation is an educated guess"]}}}, "nonreaders": [], "expdate": 1519621199000, "cdate": 1515642409425}}}, {"tddate": null, "ddate": null, "original": null, "tmdate": 1515642409442, "tcdate": 1512017672913, "number": 3, "cdate": 1512017672913, "id": "BJWfJzTez", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Official_Review", "forum": "SkffVjUaW", "replyto": "SkffVjUaW", "signatures": ["ICLR.cc/2018/Conference/Paper21/AnonReviewer2"], "readers": ["everyone"], "content": {"title": "Not sure about the novelty / contribution of the paper.", "rating": "5: Marginally below acceptance threshold", "review": "This paper aims to address the deep learning architecture search problem via incremental addition and removal of channels in intermediate layers of the network. Experiments are carried out on small-scale datasets such as MNIST and CIFAR, as well as an exploratory run on ImageNet (AlexNet).\n\nOverall, I find the approach proposed in the paper interesting but a little bit thin in content. Essentially, one increases or decreases the number of features based on equation 2. It would be much valuable to see ablation studies to show the effectiveness of such criterion: for example, simple cases one can think of is to model (1) a data distribution of known rank, (2) simple MLP/CNN models to show the cross-layer relationships (e.g. sudden increase and decrease of the number of channels across layers will be penalized by c^l_{f^{l+1}, t}), etc.\n\nThe experimentation section uses small scale datasets and as a result, it is relatively unclear how the proposed approach will perform on real-world applications. One apparent shortcoming of such approach is that training takes much longer time, and the algorithm is not easily made parallel (the sgd steps limit the level of parallelization that can be carried out). As a result, I am not sure about the applicability of the proposed approach.", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "writers": [], "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "duedate": 1511845199000, "tmdate": 1515642409425, "id": "ICLR.cc/2018/Conference/-/Paper21/Official_Review", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["ICLR.cc/2018/Conference/Paper21/Reviewers"], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/AnonReviewer1", "ICLR.cc/2018/Conference/Paper21/AnonReviewer3", "ICLR.cc/2018/Conference/Paper21/AnonReviewer2"], "reply": {"forum": "SkffVjUaW", "replyto": "SkffVjUaW", "writers": {"values": []}, "signatures": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "values": ["everyone"]}, "content": {"title": {"required": true, "order": 1, "description": "Brief summary of your review.", "value-regex": ".{0,500}"}, "review": {"required": true, "order": 2, "description": "Please provide an evaluation of the quality, clarity, originality and significance of this work, including a list of its pros and cons.", "value-regex": "[\\S\\s]{1,200000}"}, "rating": {"required": true, "order": 3, "value-dropdown": ["10: Top 5% of accepted papers, seminal paper", "9: Top 15% of accepted papers, strong accept", "8: Top 50% of accepted papers, clear accept", "7: Good paper, accept", "6: Marginally above acceptance threshold", "5: Marginally below acceptance threshold", "4: Ok but not good enough - rejection", "3: Clear rejection", "2: Strong rejection", "1: Trivial or wrong"]}, "confidence": {"required": true, "order": 4, "value-radio": ["5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature", "4: The reviewer is confident but not absolutely certain that the evaluation is correct", "3: The reviewer is fairly confident that the evaluation is correct", "2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper", "1: The reviewer's evaluation is an educated guess"]}}}, "nonreaders": [], "expdate": 1519621199000, "cdate": 1515642409425}}}, {"tddate": null, "ddate": null, "tmdate": 1515177169742, "tcdate": 1515174243651, "number": 2, "cdate": 1515174243651, "id": "rJhvF46Xf", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Official_Comment", "forum": "SkffVjUaW", "replyto": "SkffVjUaW", "signatures": ["ICLR.cc/2018/Conference/Paper21/Authors"], "readers": ["everyone"], "writers": ["ICLR.cc/2018/Conference/Paper21/Authors"], "content": {"title": "Revision including reviewer feedback", "comment": "We would like to briefly remark that there seems to have been some difficulty in posting the rebuttal as an \"official comment\" at the time. To clarify, the \"anonymous\" comments marked with \"rebuttal\" and the \"Comments for AnonReviewer3\" have been posted by this paper's authors and should be regarded as official comments. \n\nWe again thank the reviewers for their efforts and have uploaded a revised document improving upon suggested aspects wherever possible. To give a short summary we have:\n\n* included 2 suggested valuable references into related work\n* made a minor modification to the title by omitting the word \"architectures\" and instead simply writing \"neural networks\" as reviewer 1 has kindly noted that the word and concept of architectures seems to have different interpretations in the community and thus could be misleading in the title of our work. \n* added an additional appendix section discussing increase of networks' capacities beyond the reference (addressing reviewer 1). We provide an example with loss, training and validation curves to show the non-triviality of effective capacity when regularizers are present.\n* made minor modifications to the main body to further underline the novelty to the reader and avoid miss-conceptions about concepts being borrowed by \"Hao et. al.\" or other pruning papers that are not in the scope of the expansion framework presented in this work. (addressing reviewer 1)\n* simplified equation 2 with respect to the explicit indices of the norm and the spatial dimensions. We have furthermore made corresponding changes to the description of the equation to portray the cross-correlation concept earlier. This should improve readability and understanding of the equation. (addressing reviewer 3) \n* added a section in the appendix addressing the possibility and open questions of applying our proposed framework without the need for re-initialization. The section should further clarify why we have decided to not include a demo of such an experiment as we believe it would lead to potentially misleading results and interpretation. (addressing reviewer 3) \n* made minor modifications to wording and corrected some few typos. \n* rephrased a short part about the computational perspective of our approach to emphasize the approach's modularity and potential for parallelization with no limitations known to us beyond regular SGD optimization (addressing reviewer 2)\n\nUnfortunately we have not been able to include the request made by reviewer 2: \"data distribution of known rank and simple models to show cross-layer relationships\". We have thought long and hard about this statement and could not come to a conclusion of how to conduct such an experiment in a convincing manner. We believe that such experiments about cross-layer relationships are absolutely desirable, but still an open-challenge for deep learning in general and thus not immediate to our contribution. We have requested some clarification about the nature of such experiments and did not yet receive further explanation. Independent of the decision of acceptance of our work we would be extremely grateful if the reviewer could extend and clarify the review so that we can draw more value from it and include it in future work and improvements. \n\nAs a last remark we would like to again point out our concern with the very harsh lack of novelty statement made by reviewer 1. The reviewer seems to believe our mechanism is \"borrowed\" from Hao et al's paper, which is concerned _only_ with pruning of already _trained_ networks, and voices correspondingly harsh feedback about the value (or lack there-of) of our bottom-up expansion approach. We are particularly concerned with the one-sided nature of statements such as \"one should wait for the model to converge, and then determine what is useful and what is not.\". Independent of whether such a statement turns out to be true, we strongly believe that exploration of alternatives (one presented here) to always training networks to full convergence before making modifications is crucial and provides necessary insights beyond \"pushing benchmark numbers\". \n"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1516825740509, "id": "ICLR.cc/2018/Conference/-/Paper21/Official_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+|ICLR.cc/2018/Conference/Paper21/Authors|ICLR.cc/2018/Conference/Paper21/Area_Chair|ICLR.cc/2018/Conference/Program_Chairs"}, "signatures": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+|ICLR.cc/2018/Conference/Paper21/Authors|ICLR.cc/2018/Conference/Paper21/Area_Chair|ICLR.cc/2018/Conference/Program_Chairs", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Paper21/Authors_and_Higher", "ICLR.cc/2018/Conference/Paper21/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Paper21/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": [], "invitees": ["ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Area_Chair", "ICLR.cc/2018/Conference/Program_Chairs"], "cdate": 1516825740509}}}, {"tddate": null, "ddate": null, "tmdate": 1513262420203, "tcdate": 1513262420203, "number": 5, "cdate": 1513262420203, "id": "Bk2UTZgGM", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "forum": "SkffVjUaW", "replyto": "SkvTjWqxG", "signatures": ["(anonymous)"], "readers": ["everyone"], "writers": ["(anonymous)"], "content": {"title": "Reference to Farley et al. \"Self-informed neural network structure learning\" ", "comment": "We welcome the additional reference to Farley et al\u2019s work. We went through it carefully and believe that the work contains some great ideas. We would also like to point out that the scope of \u201cSelf-informed neural network structure learning\u201d is different and in fact orthogonal to the work presented here. \n\nOur work is complementary in a sense that it could be used as a precursor. Farley et al. show how to adapt/transfer already well-performing trained networks by doing capacity increases (e.g. with a large ImageNet trained GoogLeNet), whereas our work tackles the challenge of coming up with suitable capacity and feature spaces of such a network in the first place. In our understanding, Farley et al\u2019s work does not seem to focus on the question of whether the underlying trained neural network\u2019s capacity is appropriate in the first place and relies on this factor as given. \nIn this sense, our proposed method is valuable in construction of the initial feature space (from very small to larger more adequate) capacity on a task, and the method suggested by Farley could offer incremental capacity addition on top of the converged architecture when moving to novel data. We had thus initially not cited this work, but will include a reference to Farley et al in the related work section as a valuable orthogonal idea.\n"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1512791694920, "id": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["~"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "~.*|\\(anonymous\\)"}, "signatures": {"values-regex": "~.*|\\(anonymous\\)", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Authors_and_Higher", "ICLR.cc/2018/Conference/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Area_Chair"], "cdate": 1512791694920}}}, {"tddate": null, "ddate": null, "tmdate": 1513261563628, "tcdate": 1513261563628, "number": 4, "cdate": 1513261563628, "id": "HyNZcZxfG", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "forum": "SkffVjUaW", "replyto": "SkvTjWqxG", "signatures": ["(anonymous)"], "readers": ["everyone"], "writers": ["(anonymous)"], "content": {"title": "AnonReviewer1 Rebuttal", "comment": "Thank you for taking the time to read our work and write this review. We share the view that automated neural network design holds large promise. Concerning the five points we are a little dismayed by the statements.\n\n1.) In our opinion the word \u201carchitecture\u201d doesn\u2019t have a rigid definition and can span a variety of concepts. We have chosen the title because we investigate different neural networks and notice common patterns in formation of feature space dimensionality. We think that the abstract makes the scope of the paper quite clear. If it is allowed, we can imagine omitting the word \u201carchitecture\u201d in the title. We believe this should clear the confusion.\n\n2.) >\u201clack of novelty\u201d and the statement that our work is largely \u201cborrowed\u201d.\n While Hao et al. provided inspiration, there are several crucial differences to \u201cPruning Filters for Efficient Convnets\u201d:\n \n-Hao et al ONLY talks about pruning already trained NNs. Our metric follows in spirit by observing entire filters instead of individual weight values. But while Hao et al base pruning on filter magnitudes, we look at the evolution over time in a normalized fashion. Due to this change we can move to a BOTTOM-UP expansion approach instead of pruning. This is fundamentally different from any pruning paper. We do NOT present this work as a technique for pruning at all.\n\n-\u201c while only the expansion mechanism is now normalized to avoid re-scaling issues and threshold tuning.\u201d\nThe expansion mechanism itself is novel and only works due to the added idea of normalization. To the best of our knowledge this has not been proposed in previous works. Works in the spirit of Hao et al. take top-down approaches where it is always required to train a neural network to full convergence first. A network\u2019s feature dimensionality had to first be picked through large scale empirical experimentation and human intuition before pruning. In contrast, our work incrementally adds capacity starting from just one feature per layer. \n\n-We empirically observe alternate feature composition in comparison with the common rule of thumb for NN design of adding features towards deeper layers. We speculate that this could play a role in future NN design.\n\n3.) >\u201cexpanding the feature depth at layer f at time t, may have non trivial effect on layer f-1 at time t + 1\u201d. \nWe agree that change in number of features in any layer has non trivial effect on the other layers. This is the primary reason why we re-initialize features every time a feature is added to avoid the introduction of non-trivial perturbations.\n\n>\u201cLocal decisions aren't always globally optimal.\u201d\nTemporal evolution of weights is very much dependent on the minimization of the global cost. In a layer that already has more than required number of features some of the features will not receive any or minor update from the SGD step. Based on our metric no further addition of features will be required. Since it is a greedy approach, we cannot guarantee global optimality, but under given regularization constraints, our approach seems to find a good solution without loss or even improvement of generalization.\n\n4.) In our opinion pruning is a good approach for parameter reduction in models. In the context of moving towards automation of network building, one has to still decide on what network size to train to convergence before pruning. Identification of suitable feature dimensionalities for unknown datasets by itself is a difficult and demanding task. To an extent, our expansion approach aims to overcome this limitation as adequate feature dimensionalities are approached in a bottom-up fashion.\n\n5.) > Model complexity: Our expansion mechanism operates on the basis of temporal evolution of weights which depends on the ability of the model to push for zero training error under regularization constraints. In some cases, e.g. 5x increase in parameters, the corresponding original models underfit on the training data with this particular hyperparameter and regularizer configuration. It is understandable that our approach adds parameters to build a model which adequately fits the training data. Arguably a model that underfits on training data (whether due to dropout, loss function regularization terms, batch normalization etc.) will not be able to generalize well either. We will add an appropriate example with loss, train and validation curves to improve the readers understanding.\n\n> Training time: We believe it is unfair to compare the time of our approach against original models. One should recognize that authors of the original models arrive at those architectures after rigorous experimental validation of many feature configurations which all together takes lot more time. Our approach on the other hand, given the depth of network, starts with one feature per layer and automatically chooses suitable feature dimensionality in one go.\n"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1512791694920, "id": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["~"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "~.*|\\(anonymous\\)"}, "signatures": {"values-regex": "~.*|\\(anonymous\\)", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Authors_and_Higher", "ICLR.cc/2018/Conference/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Area_Chair"], "cdate": 1512791694920}}}, {"tddate": null, "ddate": null, "tmdate": 1513180331818, "tcdate": 1513180331818, "number": 3, "cdate": 1513180331818, "id": "S1Nnn6A-G", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "forum": "SkffVjUaW", "replyto": "S1gFMVoeM", "signatures": ["(anonymous)"], "readers": ["everyone"], "writers": ["(anonymous)"], "content": {"title": "Comments for AnonReviewer3", "comment": "Thank you very much for taking the time to write this review.\n\n>\u201cThat said, it would be nice to demonstrate that the algorithm also works for other tasks than image classification.\u201c\n\nThank you very much for the pointer. We are planning on and will make sure to add experiments on other data types in the future.\n\n> \u201cThe fact that all parameters are re-initialised whenever any layer width changes seems odd at first, but I think it is sufficiently justified. It would be nice to see some comparison experiments as well though, as the intuitive thing to do would be to just keep the existing weights as they are.\u201d\n\nWe think that adding these experiments should only be hinted at and largely be postponed to a later version together with a more rigorous analysis. The reasoning here is similar to what we have stated in the outlook as we believe that it is necessary to do a more profound analysis of initialization techniques and the accompanied effect on convergence behavior. Given that the open questions here are of untrivial nature, we were hesitant to simply include some experiments here. \nWe are going to outline some of the concrete questions about initialization (re-initialization) more thoroughly in the future work section to give the reader a better understanding of the challenges and possibilities when moving away from re-initialization. \n\n>\u201cFormula (2) seems needlessly complicated because of all the additional indices\u201d\n\nThank you for the suggestion, we do agree that there can be less amount of indices. We chose to explicitly write down all the indices to avoid any ambiguity. We agree that we can simplify the spatial indices and only make the incoming and outcoming feature dimensionality explicit. We will update this. We will also make sure to mention that our equation is basically normalized cross-correlation right next to the formula to improve understanding as well.\n"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1512791694920, "id": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["~"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "~.*|\\(anonymous\\)"}, "signatures": {"values-regex": "~.*|\\(anonymous\\)", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Authors_and_Higher", "ICLR.cc/2018/Conference/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Area_Chair"], "cdate": 1512791694920}}}, {"tddate": null, "ddate": null, "tmdate": 1513180009422, "tcdate": 1513179904009, "number": 2, "cdate": 1513179904009, "id": "rkdbjT0Zz", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "forum": "SkffVjUaW", "replyto": "BJWfJzTez", "signatures": ["(anonymous)"], "readers": ["everyone"], "writers": ["(anonymous)"], "content": {"title": "AnonReviewer2 Rebuttal ", "comment": "Thank you very much for the review and suggestions on how to improve our work. We would like to request the reviewer for some further clarification which will help us in the improvements.\n\n> \u201cIt would be much valuable to see ablation studies to show the effectiveness of such criterion: for example, simple cases one can think of is to model (1) a data distribution of known rank, (2) simple MLP/CNN models to show the cross-layer relationships (e.g. sudden increase and decrease of the number of channels across layers will be penalized by c^l_{f^{l+1}, t}), etc.\u201d\n\nWe propose our approach as a greedy expansion method to construct a network\u2019s feature space such that it can fit underlying data under some regularization constraints. Given a fixed amount of layers, we start with one feature per layer and grow the capacity until the network is capable of adequately fitting the training data.\nWith respect to comment (1)(\u201ca data distribution of known rank\u201d), we agree that suggested analysis will be very important and further theoretical analysis will be valuable and is necessary.\nWe also believe that comment (2)(\u201cshow cross-layer relationships\u201d) addressing understanding of cross layer relationships in neural networks and analysis of multi-layer non-linear networks\u2019 feature spaces can provide further insights. \n\nFollowing the reviewer\u2019s suggestion, we can imagine conducting some toy dataset experiment of the following type: \nTake data distributions of increasing rank. Monitor and analyse the relationship to the capacity that our expansion algorithm allocates depending on rank.\nHowever it is unclear to us how such an analysis will provide more rigorous insights into the cross-layer relationships or how increase of distribution rank maps to a multi-layer non-linear neural network (even if we talk about a multi hidden layer MLP), particularly under regularization and SGD sampling. Unless we conduct such an experiment for a very shallow, linear MLP, to the best of our knowledge this will result in purely empirical insights on whether the capacity allocated by our algorithm scales (in a similar fashion as observed when moving from MNIST to CIFAR10 to CIFAR100). We would be grateful if the reviewer could further clarify his suggestion. \n \nIt is our view that the novel contributions in this work are 1) the expansion framework for network building itself, 2) conducted experiments and 3) the idea to use a normalized cross-correlation metric.\n\n>\u201cThe experimentation section uses small scale datasets \u201c\n\nWe included a few initial experiments on the large scale ImageNet dataset in the initial version submitted for the review and hope to add more in the future. We would kindly ask the reviewer to consider that running experiments on ImageNet using very large architectures like ResNets and its corresponding hardware demands is a resource challenge for many. \n\n>\u201cOne apparent shortcoming of such approach is that training takes much longer time.\u201d\n\nIt is true that our approach takes longer compared to the pure training time of corresponding original models. However, we believe that one should also take into account all the time spent by the authors of original models on validation experiments (grid-search, random-search etc.) used to find those models. In general in this paper we present that our method works consistently for the datasets tested so far.\nIf we imagine an encounter with a new (vision) dataset of unknown origin and the task to find a suitable (convolutional) neural network. Our method can provide substantial benefits in exploring architecture options by not having to choose feature dimensionality by hand and e.g. concentrating on amount of layers instead. The alternatives are all very time-consuming if the user doesn\u2019t already have a very good prior on task complexity (like on the common benchmark datasets) and often includes training of networks that initially completely over- or underfit before determining suitable upper or lower bounds on model complexity. \n\n>\u201cthe algorithm is not easily made parallel (the sgd steps limit the level of parallelization that can be carried out)\u201d\n\nTo summarize our expansion approach, we start with a network consisting of one feature per layer and begin training. Based on the temporal evolution metric proposed, features are added at each layer and training is re-initialized, very much like training a new network with increased features per layer. This process repeats until no more features are added at each layer and the final network is trained to convergence. \nIf we now compare this to traditional SGD (and its variants) with a fixed model that means that we do not interfere with the optimization, other than making the decision of whether to expand after a SGD step is taken. \n\nWe would greatly appreciate if you could further elaborate on this point as it isn\u2019t clear to us how SGD steps are limiting the level of parallelization in our approach in contrast to \u201cconventional\u201d SGD. \n"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1512791694920, "id": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["~"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "~.*|\\(anonymous\\)"}, "signatures": {"values-regex": "~.*|\\(anonymous\\)", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Authors_and_Higher", "ICLR.cc/2018/Conference/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Area_Chair"], "cdate": 1512791694920}}}, {"tddate": null, "ddate": null, "original": null, "tmdate": 1510092431326, "tcdate": 1509986803610, "number": 1, "cdate": 1509986803610, "id": "rknxzzRAb", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Official_Comment", "forum": "SkffVjUaW", "replyto": "BJ4OYp8Tb", "signatures": ["ICLR.cc/2018/Conference/Paper21/Authors"], "readers": ["everyone"], "writers": ["ICLR.cc/2018/Conference/Paper21/Authors"], "content": {"title": "Related work", "comment": "Thank you for the pointer to the ICLR 2017 paper. We were presently unaware of this paper, but after taking a brief look identified it as a relevant reference. \n\nWe will go through it more thoroughly and then add it where appropriate in the related work section. \n\nBest,"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1516825740509, "id": "ICLR.cc/2018/Conference/-/Paper21/Official_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+|ICLR.cc/2018/Conference/Paper21/Authors|ICLR.cc/2018/Conference/Paper21/Area_Chair|ICLR.cc/2018/Conference/Program_Chairs"}, "signatures": {"values-regex": "ICLR.cc/2018/Conference/Paper21/AnonReviewer[0-9]+|ICLR.cc/2018/Conference/Paper21/Authors|ICLR.cc/2018/Conference/Paper21/Area_Chair|ICLR.cc/2018/Conference/Program_Chairs", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Paper21/Authors_and_Higher", "ICLR.cc/2018/Conference/Paper21/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Paper21/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": [], "invitees": ["ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Area_Chair", "ICLR.cc/2018/Conference/Program_Chairs"], "cdate": 1516825740509}}}, {"tddate": null, "ddate": null, "tmdate": 1508460908080, "tcdate": 1508460908080, "number": 1, "cdate": 1508460908080, "id": "BJ4OYp8Tb", "invitation": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "forum": "SkffVjUaW", "replyto": "SkffVjUaW", "signatures": ["~W._James_Murdoch1"], "readers": ["everyone"], "writers": ["~W._James_Murdoch1"], "content": {"title": "Related work", "comment": "I think this paper from ICLR 2017 may be relevant to your work, and is probably worth adding to your related work section.\n\nBest of luck\n\nhttps://www.cs.cmu.edu/~jgc/publication/Nonparametric%20Neural%20Networks.pdf"}, "nonreaders": [], "details": {"replyCount": 0, "writable": false, "overwriting": [], "revisions": false, "forumContent": {"title": "Building effective deep neural networks one feature at a time", "abstract": "Successful training of convolutional neural networks is often associated with suffi-\nciently deep architectures composed of high amounts of features. These networks\ntypically rely on a variety of regularization and pruning techniques to converge\nto less redundant states. We introduce a novel bottom-up approach to expand\nrepresentations in fixed-depth architectures. These architectures start from just a\nsingle feature per layer and greedily increase width of individual layers to attain\neffective representational capacities needed for a specific task. While network\ngrowth can rely on a family of metrics, we propose a computationally efficient\nversion based on feature time evolution and demonstrate its potency in determin-\ning feature importance and a networks\u2019 effective capacity. We demonstrate how\nautomatically expanded architectures converge to similar topologies that benefit\nfrom lesser amount of parameters or improved accuracy and exhibit systematic\ncorrespondence in representational complexity with the specified task. In contrast\nto conventional design patterns with a typical monotonic increase in the amount of\nfeatures with increased depth, we observe that CNNs perform better when there is\nmore learnable parameters in intermediate, with falloffs to earlier and later layers.", "pdf": "/pdf/8100928dc43121b2c543537f6f03fd071fdd8180.pdf", "TL;DR": "A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity.", "paperhash": "mundt|building_effective_deep_neural_networks_one_feature_at_a_time", "_bibtex": "@misc{\nmundt2018building,\ntitle={Building effective deep neural networks one feature at a time},\nauthor={Martin Mundt and Tobias Weis and Kishore Konda and Visvanathan Ramesh},\nyear={2018},\nurl={https://openreview.net/forum?id=SkffVjUaW},\n}", "keywords": ["convolution neural networks", "architecture search", "meta-learning", "representational capacity"], "authors": ["Martin Mundt", "Tobias Weis", "Kishore Konda", "Visvanathan Ramesh"], "authorids": ["mundt@fias.uni-frankfurt.de", "weis@ccc.cs.uni-frankfurt.de", "kishore.konda@insofe.edu.in", "ramesh@fias.uni-frankfurt.de"]}, "tags": [], "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1512791694920, "id": "ICLR.cc/2018/Conference/-/Paper21/Public_Comment", "writers": ["ICLR.cc/2018/Conference"], "signatures": ["ICLR.cc/2018/Conference"], "readers": ["everyone"], "invitees": ["~"], "reply": {"replyto": null, "forum": "SkffVjUaW", "writers": {"values-regex": "~.*|\\(anonymous\\)"}, "signatures": {"values-regex": "~.*|\\(anonymous\\)", "description": "How your identity will be displayed with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "value-dropdown": ["everyone", "ICLR.cc/2018/Conference/Authors_and_Higher", "ICLR.cc/2018/Conference/Reviewers_and_Higher", "ICLR.cc/2018/Conference/Area_Chairs_and_Higher", "ICLR.cc/2018/Conference/Program_Chairs"]}, "content": {"title": {"required": true, "order": 0, "description": "Brief summary of your comment.", "value-regex": ".{1,500}"}, "comment": {"required": true, "order": 1, "description": "Your comment or reply (max 5000 characters).", "value-regex": "[\\S\\s]{1,5000}"}}}, "nonreaders": [], "noninvitees": ["ICLR.cc/2018/Conference/Paper21/Authors", "ICLR.cc/2018/Conference/Paper21/Reviewers", "ICLR.cc/2018/Conference/Paper21/Area_Chair"], "cdate": 1512791694920}}}], "count": 12} |
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{
"name": "SkyCal",
"version": "0.0.1",
"description": "Calendar app of astronomical events.",
"private": true,
"author": "Joseph Watters",
"license": "MIT",
"repository": {
"type": "git",
"url": "https://github.com/imjohspe/skycal.git"
},
"main": "index.js",
"scripts": {
"test": "better-npm-run test",
"test:cov": "better-npm-run test:cov",
"start": "better-npm-run start",
"build:start": "better-npm-run build:start",
"build": "rimraf dist && better-npm-run build",
"release": "npm run check-style && npm run test && npm run build && rimraf gh-pages && mkdir gh-pages && cd gh-pages && cp -r ../dist/* . && git init . && git remote add origin git@github.com:vesparny/react-kickstart.git && git checkout -b gh-pages && git add . && git commit -m 'publishing' && git push origin gh-pages -f",
"lint": "standard",
"changelog": "conventional-changelog -p angular -i CHANGELOG.md -s",
"c": "git-cz"
},
"standard": {
"globals": [
"__DEV__"
],
"plugins": [
"react"
],
"rules": {
"react/jsx-no-bind": 2,
"react/no-did-update-set-state": 2,
"react/no-unknown-property": 2,
"react/prop-types": 2
}
},
"betterScripts": {
"start": {
"command": "node devServer",
"env": {
"NODE_ENV": "development"
}
},
"build": {
"command": "webpack --config webpack.config.prod.js --progress --profile --colors",
"env": {
"NODE_ENV": "production"
}
},
"build:start": {
"command": "node prodServer",
"env": {
"NODE_ENV": "production"
}
}
},
"dependencies": {
"fastclick": "^1.0.6",
"express": "^4.13.4",
"jquery": "^2.2.1",
"mongoose": "^4.4.7",
"react": "^0.14.7",
"react-dom": "^0.14.7",
"react-router": "^2.0.0",
"rimraf": "^2.5.2",
"better-npm-run": "0.0.7"
},
"devDependencies": {
"autoprefixer": "^6.3.3",
"babel-cli": "^6.5.1",
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{"poster":"INNTR Rebel","date":"2018-09-17T10:33:37.144+0000","title":"Mid Perdoname ( Espero que guste )","subforum":"Streaming & videos","up_votes":19,"down_votes":4,"body":"https://www.youtube.com/watch?v=XiUkgts6a2c&feature=youtu.be","replies":[{"poster":"ManuelAlcayaga","date":"2018-09-17T11:18:09.571+0000","up_votes":3,"down_votes":0,"body":"+1! buenísimo!!\n{{sticker:slayer-pantheon-thumbs}}","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T11:59:46.339+0000","up_votes":1,"down_votes":0,"body":"Gracias hermano..y gracias por estar en esta nueva etapa creativa jaj","replies":[]}]},{"poster":"r0y","date":"2018-09-18T05:40:35.082+0000","up_votes":2,"down_votes":0,"body":"Buen tema, menos mal no soy jg o estaría llorando por todos los mid que no gnkkk{{sticker:slayer-pantheon-thumbs}}","replies":[{"poster":"INNTR Rebel","date":"2018-09-18T11:15:20.641+0000","up_votes":1,"down_votes":0,"body":"Jajajajajajajaj\nGracias hermano","replies":[]}]},{"poster":"Tebepe","date":"2018-09-17T19:35:30.597+0000","up_votes":2,"down_votes":0,"body":"***","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T20:02:11.598+0000","up_votes":1,"down_votes":0,"body":"{{sticker:galio-happy}}","replies":[]}]},{"poster":"ÉspermaDeMadLife","date":"2018-09-17T17:23:25.005+0000","up_votes":2,"down_votes":0,"body":"que buen trap cristiano like","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T18:45:17.654+0000","up_votes":1,"down_votes":0,"body":"Gracias amigo!Vamos a ver con que me inspiro la prox jaj","replies":[]}]},{"poster":"Juice Wrld","date":"2018-09-17T13:53:31.808+0000","up_votes":2,"down_votes":1,"body":"Como embarrar un buen tema by Rebel Clown","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T13:58:17.361+0000","up_votes":1,"down_votes":1,"body":"Gracias por poner mi parodia a nivel del tema original para llegar a decir de embarrar,ademas de que se trata de una PARODIA jaj abrazo bro.","replies":[{"poster":"Juice Wrld","date":"2018-09-17T14:09:52.003+0000","up_votes":2,"down_votes":1,"body":"Largas tremendo olor a cosco armi","replies":[{"poster":"destroyer222","date":"2018-09-17T15:06:54.882+0000","up_votes":2,"down_votes":0,"body":"Sos alto ortiva, jajaxd","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T20:11:45.416+0000","up_votes":1,"down_votes":0,"body":"Jajaja {{sticker:zombie-nunu-tears}}","replies":[]}]}]}]}]},{"poster":"destroyer222","date":"2018-09-17T15:05:26.493+0000","up_votes":2,"down_votes":0,"body":"Jajajajajaaj esta epico amigo!","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T15:10:34.620+0000","up_votes":1,"down_votes":0,"body":"Jajaj Gracias Hun1 ! Se dio lo mejor ! Jaj","replies":[]}]},{"poster":"Rebkans","date":"2018-09-17T13:54:55.564+0000","up_votes":2,"down_votes":0,"body":"Está muy bueno hno!!\n\nTenés una buena voz para cantar, no como otros que cantan como perro y se creen Elvis Presley\n\n+1","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T13:59:40.929+0000","up_votes":2,"down_votes":0,"body":"Jajaja gracias mi hermano.La verdad que siempre le tuve ganas en hacer estas cosas.\nY aca estoy!gracias por el comentario,enserio.","replies":[]}]},{"poster":"kid shadow","date":"2018-09-17T12:59:38.615+0000","up_votes":2,"down_votes":0,"body":"No soy fan de este estilo musical pero.... +1 para ti campeón por que el tema esta buenísimo je.{{sticker:slayer-pantheon-thumbs}}","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T13:56:08.420+0000","up_votes":1,"down_votes":0,"body":"Gracias por el comentario !! Me gustaria saber que estilo te gusta para ver si puedo crear algo ! Abrazo !","replies":[]}]},{"poster":"Omori","date":"2018-09-17T17:24:58.922+0000","up_votes":2,"down_votes":1,"body":"Cuando no gankee mid le voy a mandar esto al final de la partida \n{{sticker:sg-ezreal}}","replies":[{"poster":"INNTR Rebel","date":"2018-09-17T18:43:58.434+0000","up_votes":1,"down_votes":0,"body":"Esta buena la idea jaja ! Resumirlo todo en una cancion jaj","replies":[]}]},{"poster":"Tokita Ohma","date":"2018-09-18T00:35:30.440+0000","up_votes":1,"down_votes":1,"body":"***","replies":[{"poster":"INNTR Rebel","date":"2018-09-18T01:45:09.664+0000","up_votes":1,"down_votes":0,"body":"Gracias !","replies":[]}]}]} |
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"4 oz cream cheese, softened",
"1/2 cup smooth peanut butter",
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"2 tablespoons dark brown sugar",
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"1/2 teaspoon vanilla",
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{"poster":"taeguki","date":"2017-08-29T20:28:14.453+0000","title":"NEW PINGS? maybe a \"gj\" ping would help...","subforum":"Champions & Gameplay","up_votes":3,"down_votes":1,"body":"When Riot implemented the new "_Vision Ping_" into the game, I started thinking about other pings that might be useful. \r\n\r\nOne immediately came to my mind: a "_Good Job Ping_" of some sort. Often i find myself typing "_wp_" or "_gj_" into the chat, but this could be made a lot easier by just adding a "_Well Played_" or "_Good Job Ping_". It could just display a "_thumbs up_" symbol or something.\r\n\r\nThis might make games more enjoyable as people stay more positive because of the good "feedback" they are getting.\r\n\r\nTell me what you think below pls.","replies":[{"poster":"Wekra","date":"2017-08-29T21:12:30.739+0000","up_votes":2,"down_votes":1,"body":"First of all this is the german forum but nevermind.\n\ntyping\"gj\" needs 2 secs, but pinging in bush and typing there is a ward or a vision ward will need more time including the fact, the mate might not see the ping where is the ward, so this new ping is quite useful.\n\ngj is not relevant in time, it doesnt matter if you write it immediately or 20 secs later, but when a jungler comes around u need the time to prepare the gank and give him the vision information..\n\nincluding the fact that it would make the game more complex if we add to many unnecessary pings","replies":[]},{"poster":"Hakkuu","date":"2017-08-29T23:14:11.284+0000","up_votes":1,"down_votes":0,"body":"Basically a good idea. Too many pings are too complicated to use tho - You already have around 4 buttons for different pings","replies":[{"poster":"taeguki","date":"2017-09-02T14:14:26.632+0000","up_votes":1,"down_votes":0,"body":"I see your point but I`d say that the ping does not make the game more complicated. It is no ping that needs to be used in order to have better teamplay from a gameplay perspective, I just thought it would be a neat thing to have for us lazy people. If you want to use it you can, but if you do not it doesn`t make the teamplay any worse.","replies":[]}]},{"poster":"Sihari","date":"2017-08-29T22:24:46.409+0000","up_votes":1,"down_votes":0,"body":"I dont think that we will need a \"GJ\"-Ping. \nIn the near future we will get the new emote-system, which will allow us to show a quick \"thumbs up\" after a nice play~ Having a ping for the same thing would be redundant.","replies":[{"poster":"taeguki","date":"2017-09-02T14:10:35.205+0000","up_votes":1,"down_votes":0,"body":"An emote basically does the same thing, so i agree with you on that point.","replies":[]}]}]} |
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{"poster":"Nocere Æternum","date":"2019-08-07T14:30:59.531+0000","title":"duo para salir de plata?","subforum":"Reclutamiento","up_votes":1,"down_votes":0,"body":"estoy de plata 1 80 a plata 2 00 pl como monta;a rusa hace como un mes , juego jg y adc, coparia un duo mid o sup nose :/ alguien se copa?","replies":[{"poster":"kevingstonXD","date":"2019-08-08T03:55:27.989+0000","up_votes":1,"down_votes":0,"body":"Soy Oro III main Jg (Champ) VI, Sejuani, Warwinck, Graves, Zac y Jax. Linea secundaria Mid (Champ) Oriana, Ahri, Talon y Leblanc. Culaquier cosa interesado que me agregue desde ya muchas gracias.","replies":[]},{"poster":"Desatormentado","date":"2019-08-07T14:36:41.655+0000","up_votes":1,"down_votes":0,"body":"Yo! ahi te mandé","replies":[]},{"poster":"VenganceZ","date":"2019-08-07T22:20:41.053+0000","up_votes":1,"down_votes":1,"body":"Yo tengo altas ganas pero primero tengo que salir de bronce no?","replies":[]}]} |
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{"poster":"fernaso89","date":"2015-10-10T14:22:15.263+0000","title":"Squadra Classificata","subforum":"Reclutamento","up_votes":2,"down_votes":2,"body":"Sto cercando qualcuno che fondi con me una squadra classificata, sono un main top in bronzo 4(ma copro altri ruoli come supp e jgl), ma sono stanco di questa lega per i continui troll ecc ecc, sapete meglio di me come va. \r\nSe siete interessati scrivetemi qui, il mio nickname è fernaso89.\r\n\r\nCiao :)","replies":[{"poster":"PiccoloRumy","date":"2015-10-11T16:08:23.499+0000","up_votes":1,"down_votes":0,"body":"Ciao io ci sono main mid e supp ti aggiungo","replies":[]},{"poster":"KILLER0ITA","date":"2015-10-10T20:32:41.447+0000","up_votes":1,"down_votes":0,"body":"Aggiungimi, KILLER0ITA , riguardo al top ce ne servirebbe uno. Tu aggiungimi che poi ti testiamo e ti sapremo dire ( solo per vedere che non sei un feeder ). Usiamo TS. Ciao :)","replies":[]},{"poster":"Endermen99","date":"2015-10-10T15:01:43.244+0000","up_votes":1,"down_votes":0,"body":"Io ci sono\nMain supp/jung","replies":[]}]} |
{"poster":"Daxthor50","date":"2015-08-24T19:06:38.640+0000","title":"creazione champ","subforum":"Campioni e gameplay","up_votes":1,"down_votes":1,"body":"ciao a tutti, siccome è la prima volta che creo un argomento quì mi chiedevo, se foste VOI ad avere in mente un champ personalizzato, conoscereste dei programmi per disegnarlo avendo modelli?, io ne ho in mente un paio, ma fin ora non ho trovato nnt","replies":[{"poster":"kirchhoff","date":"2015-08-28T20:53:12.496+0000","up_votes":1,"down_votes":0,"body":"Blender?","replies":[]}]} |
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