{"id":4237,"date":"2023-12-31T23:00:00","date_gmt":"2023-12-31T14:00:00","guid":{"rendered":"https:\/\/devneko.jp\/wordpress\/?p=4237"},"modified":"2023-12-31T23:00:00","modified_gmt":"2023-12-31T14:00:00","slug":"2023%e5%b9%b4%e3%81%aearxiv","status":"publish","type":"post","link":"https:\/\/devneko.jp\/wordpress\/?p=4237","title":{"rendered":"2023\u5e74\u306earXiv"},"content":{"rendered":"\n

2023\u5e74\u3001\u72ec\u81ea\u306e\u30b9\u30b3\u30a2\u30e9\u30f3\u30ad\u30f3\u30b0\u4e0a\u4f4d\u306e\u8ad6\u6587\u3092\u96c6\u8a08\u3057\u3066\u307f\u305f\u30022023\u5e74\u30c8\u30c3\u30d7\u306fGemini\u306e\u8ad6\u6587\uff08Fugu-MT \u8ad6\u6587\u7ffb\u8a33(\u6982\u8981): Gemini: A Family of Highly Capable Multimodal Models (fugumt.com)<\/a>\uff09\u3060\u3063\u305f\u3002\u8457\u8005\u6570\u304b\u3089\u8a00\u3063\u3066\u3082\u59a5\u5f53\u3067\u3042\u308d\u3046\u3068\u601d\u3046\u3002\u8457\u8005\u306e\u591a\u3044Survey\u304c\u4e0a\u4f4d\u306b\u6765\u308b\u50be\u5411\u304c\u3042\u308b\u304c\u3001\u305d\u306e\u4ed6\u3082\u6ce8\u76ee\u3059\u3079\u304d\u8ad6\u6587\u304c\u591a\u3044\u3002<\/p>\n\n\n\n

\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u306f\u8ad6\u6587\u8457\u8005\u304c\u30c8\u30c3\u30d7\u30ab\u30f3\u30d5\u30a1\u30ec\u30f3\u30b9\u306b\u901a\u3057\u305f\u767a\u8868\u6570\u30fb\u30c8\u30c3\u30d7\u8ad6\u6587\u8a8c\u306b\u901a\u3057\u305f\u8ad6\u6587\u6570\u3092\u5143\u306b\u7b97\u51fa\u3057\u3066\u3044\u308b\u3002\u6709\u540d\u7814\u7a76\u8005\u304c\u591a\u6570\u53c2\u52a0\u3059\u308b\u8ad6\u6587\u306e\u30b9\u30b3\u30a2\u304c\u4e0a\u304c\u308a\u3084\u3059\u3044\u3002\u306a\u304a\u3001\u4eca\u307e\u3067\u306e\u6700\u9ad8\u30b9\u30b3\u30a2\u306fBIG-bench\uff08Fugu-MT \u8ad6\u6587\u7ffb\u8a33(\u6982\u8981): Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (fugumt.com)<\/a>\uff09\u306e649.7\u3067\u3042\u308b\u3002<\/p>\n\n\n\n

\u4eca\u5e74Fugu-MT: arxiv\u306e\u8ad6\u6587\u7ffb\u8a33(\u6982\u8981) (fugumt.com)<\/a>\u3067\u6271\u3063\u305f\u8ad6\u6587\u306f\u521d\u56de\u30d0\u30fc\u30b8\u30e7\u30f3\u306e\u307f\u3092\u6570\u3048\u306679862\u4ef6\u3001\u30d0\u30fc\u30b8\u30e7\u30f3\u30a2\u30c3\u30d7\u5206\u3092\u500b\u5225\u6271\u3044\u306b\u3059\u308b\u3068108555\u4ef6\u3060\u3063\u305f\u30022022\u5e74\u306f61378\u4ef6\u300179010\u4ef6\u3060\u3063\u305f\u3053\u3068\u304b\u3089\u3053\u306e\u5206\u91ce\u304c\u3055\u3089\u306b\u76db\u308a\u4e0a\u304c\u308a\u3092\u898b\u305b\u3066\u3044\u308b\u304barXiv\u306e\u91cd\u8981\u6027\u304c\u4e0a\u304c\u3063\u3066\u3044\u308b\u3088\u3046\u3002\u3044\u305a\u308c\u306b\u3057\u3066\u3082arXiv\u3060\u3051\u3067\u66f4\u65b0\u5206\u4f75\u305b\u30669000\u4ef6\/\u6708\u3001300\u4ef6\/\u65e5\u3068\u591a\u304f\u306e\u8ad6\u6587\u304c\u6295\u7a3f\u3055\u308c\u3066\u3044\u3066\u60c5\u5831\u3092\u8ffd\u3046\u306e\u306f\u306a\u304b\u306a\u304b\u5927\u5909\u306b\u306a\u3063\u3066\u3044\u308b\u3068\u601d\u3046\u3002<\/p>\n\n\n\n

2023\u5e74\u767a\u8868\u3067\u30b9\u30b3\u30a2\u304c\u9ad8\u304b\u3063\u305f\u8ad6\u6587\u4e0a\u4f4d10\u4ef6<\/h3>\n\n\n\n
    \n
  1. Gemini: A Family of Highly Capable Multimodal Models\u00a0<\/strong>[517.1]
    \u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u30e2\u30c7\u30eb\u306e\u65b0\u305f\u306a\u30d5\u30a1\u30df\u30ea\u30fc\u3067\u3042\u308bGemini\u306f\u3001\u753b\u50cf\u3001\u30aa\u30fc\u30c7\u30a3\u30aa\u3001\u30d3\u30c7\u30aa\u3001\u30c6\u30ad\u30b9\u30c8\u7406\u89e3\u306b\u307e\u305f\u304c\u308b\u512a\u308c\u305f\u6a5f\u80fd\u3092\u793a\u3057\u3066\u3044\u308b\u3002 \u30d5\u30a1\u30df\u30ea\u30fc\u306fUltra\u3001Pro\u3001Nano\u30b5\u30a4\u30ba\u3067\u69cb\u6210\u3055\u308c\u3066\u304a\u308a\u3001\u8907\u96d1\u306a\u63a8\u8ad6\u30bf\u30b9\u30af\u304b\u3089\u30aa\u30f3\u30c7\u30d0\u30a4\u30b9\u30e1\u30e2\u30ea\u306b\u5236\u7d04\u306e\u3042\u308b\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u307e\u3067\u5e45\u5e83\u3044\u7528\u9014\u306b\u9069\u3057\u3066\u3044\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Tue, 19 Dec 2023 02:39:27 GMT)<\/li>\n\n\n\n
  2. A Survey of Reasoning with Foundation Models\u00a0<\/strong>[235.7]
    \u63a8\u8ad6\u306f\u3001\u4ea4\u6e09\u3001\u533b\u7642\u8a3a\u65ad\u3001\u5211\u4e8b\u635c\u67fb\u306a\u3069\u3001\u69d8\u3005\u306a\u73fe\u5b9f\u4e16\u754c\u306e\u74b0\u5883\u306b\u304a\u3044\u3066\u91cd\u8981\u306a\u5f79\u5272\u3092\u62c5\u3063\u3066\u3044\u308b\u3002 \u57fa\u790e\u30e2\u30c7\u30eb\u306e\u958b\u767a\u304c\u9032\u307f\u3001\u63a8\u8ad6\u30bf\u30b9\u30af\u306e\u80fd\u529b\u3092\u63a2\u7a76\u3059\u308b\u3053\u3068\u3078\u306e\u95a2\u5fc3\u304c\u9ad8\u307e\u3063\u3066\u3044\u308b\u3002 \u672c\u7a3f\u3067\u306f,\u69d8\u3005\u306a\u63a8\u8ad6\u30bf\u30b9\u30af,\u30e1\u30bd\u30c3\u30c9,\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u306e\u6700\u65b0\u306e\u9032\u6b69\u3092\u6d6e\u304d\u5f6b\u308a\u306b\u3057\u3066,\u63a8\u8ad6\u306b\u9069\u5fdc\u3059\u308b\u57fa\u790e\u30e2\u30c7\u30eb\u3092\u63d0\u6848\u3059\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Sun, 17 Dec 2023 15:16:13 GMT)<\/li>\n\n\n\n
  3. Graph Transformer GANs for Graph-Constrained House Generation\u00a0<\/strong>[223.7]
    \u672c\u7a3f\u3067\u306f,\u30b0\u30e9\u30d5\u30ce\u30fc\u30c9\u95a2\u4fc2\u3092\u52b9\u679c\u7684\u306b\u5b66\u7fd2\u3059\u308b\u305f\u3081\u306b,GTGAN(Graph Transformer Generative Adversarial Network)\u3092\u63d0\u6848\u3059\u308b\u3002 GTGAN\u306f\u3001\u30b0\u30e9\u30d5\u5236\u7d04\u306e\u3042\u308b\u4f4f\u5b85\u751f\u6210\u30bf\u30b9\u30af\u306b\u304a\u3044\u3066\u3001\u30a8\u30f3\u30c9\u30c4\u30fc\u30a8\u30f3\u30c9\u3067\u52b9\u7387\u7684\u306a\u30b0\u30e9\u30d5\u30ce\u30fc\u30c9\u95a2\u4fc2\u3092\u5b66\u7fd2\u3059\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Tue, 14 Mar 2023 20:35:45 GMT)<\/li>\n\n\n\n
  4. Adaptive Siamese Tracking with a Compact Latent Network\u00a0<\/strong>[219.4]
    \u8ffd\u8de1\u30bf\u30b9\u30af\u3092\u5206\u985e\u306b\u5909\u63db\u3059\u308b\u3053\u3068\u3067,\u30b7\u30fc\u30e0\u30ba\u3092\u30d9\u30fc\u30b9\u3068\u3057\u305f\u30c8\u30e9\u30c3\u30ab\u30fc\u3092\u7c21\u6613\u5316\u3059\u308b\u76f4\u611f\u7684\u306a\u30d3\u30e5\u30fc\u3092\u63d0\u4f9b\u3059\u308b\u3002 \u305d\u3053\u3067\u672c\u7814\u7a76\u3067\u306f,\u8996\u899a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u5b9f\u8ffd\u8de1\u4f8b\u3092\u7528\u3044\u3066,\u8a73\u7d30\u306a\u89e3\u6790\u3092\u884c\u3046\u3002 \u305d\u3053\u3067\u6211\u3005\u306f,\u53e4\u5178\u7684\u306aSiamRPN++,SiamFC,SiamBAN\u306e3\u3064\u306e\u30c8\u30e9\u30c3\u30ab\u30fc\u3092\u8abf\u6574\u3057\u305f\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Thu, 2 Feb 2023 08:06:02 GMT)<\/li>\n\n\n\n
  5. Regeneration Learning: A Learning Paradigm for Data Generation\u00a0<\/strong>[215.0]
    \u518d\u751f\u5b66\u7fd2\u306f\u3001\u30c7\u30fc\u30bf\u751f\u6210\u306e\u305f\u3081\u306e\u5bfe\u8c61\u30c7\u30fc\u30bfY\u306e\u62bd\u8c61\u5316(Y\u2019)\u3092\u51e6\u7406\u3059\u308b\u3002 \u518d\u751f\u5b66\u7fd2\u306f\u30c7\u30fc\u30bf\u751f\u6210\u306e\u30d1\u30e9\u30c0\u30a4\u30e0\u3068\u3057\u3066\u5e83\u304f\u5229\u7528\u3055\u308c\u3066\u3044\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Sat, 21 Jan 2023 01:33:34 GMT)<\/li>\n\n\n\n
  6. Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems\u00a0<\/strong>[211.4]
    \u79d1\u5b66\u306e\u305f\u3081\u306eAI(AI4Science)\u3068\u3057\u3066\u77e5\u3089\u308c\u308b\u65b0\u3057\u3044\u7814\u7a76\u9818\u57df \u9818\u57df\u306f\u3001\u7269\u7406\u4e16\u754c(\u6ce2\u52d5\u95a2\u6570\u3068\u96fb\u5b50\u5bc6\u5ea6)\u3001\u539f\u5b50(\u5206\u5b50\u3001\u30bf\u30f3\u30d1\u30af\u8cea\u3001\u7269\u8cea\u3001\u76f8\u4e92\u4f5c\u7528)\u3001\u30de\u30af\u30ed(\u6d41\u4f53\u3001\u6c17\u5019\u3001\u5730\u4e0b)\u307e\u3067\u7406\u89e3\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u308b\u3002 \u4e3b\u8981\u306a\u8ab2\u984c\u306f\u3001\u7269\u7406\u7b2c\u4e00\u539f\u7406\u3001\u7279\u306b\u5bfe\u79f0\u6027\u3092\u6df1\u5c64\u5b66\u7fd2\u6cd5\u306b\u3088\u3063\u3066\u81ea\u7136\u30b7\u30b9\u30c6\u30e0\u3067\u6349\u3048\u308b\u65b9\u6cd5\u3067\u3042\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Mon, 17 Jul 2023 12:14:14 GMT)<\/li>\n\n\n\n
  7. Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation\u00a0<\/strong>[210.5]
    \u610f\u5473\u7684\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u306e\u6559\u5e2b\u306a\u3057\u30c9\u30e1\u30a4\u30f3\u9069\u5fdc(UDA)\u306b\u3064\u3044\u3066\u691c\u8a0e\u3059\u308b\u3002 \u5bfe\u8c61\u9818\u57df\u304b\u3089\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b5\u30f3\u30d7\u30eb\u306e\u5272\u5408\u3092\u6e1b\u5c11\u30fb\u5897\u52a0\u3055\u305b\u308b\u3053\u3068\u3067,\u300c\u5b66\u7fd2\u80fd\u529b\u300d\u304c\u5f37\u5316\u30fb\u5f31\u307e\u308b\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u3002 \u672c\u7a3f\u3067\u306f,DTS(Double teacher-student)\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3092\u63d0\u6848\u3057,\u53cc\u65b9\u5411\u5b66\u7fd2\u6226\u7565\u3092\u53d6\u308a\u5165\u308c\u305f\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Thu, 16 Mar 2023 05:04:10 GMT)<\/li>\n\n\n\n
  8. DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies\u00a0<\/strong>[198.6]
    DeepSpeed4Science\u306f\u3001AI\u30b7\u30b9\u30c6\u30e0\u6280\u8853\u9769\u65b0\u3092\u901a\u3058\u3066\u30e6\u30cb\u30fc\u30af\u306a\u6a5f\u80fd\u3092\u69cb\u7bc9\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u3044\u308b\u3002 \u6211\u3005\u306f\u3001\u69cb\u9020\u751f\u7269\u5b66\u7814\u7a76\u306b\u304a\u3051\u308b2\u3064\u306e\u91cd\u8981\u306a\u30b7\u30b9\u30c6\u30e0\u8ab2\u984c\u306b\u5bfe\u51e6\u3059\u308b\u305f\u3081\u306b\u3001DeepSpeed4Science\u3067\u884c\u3063\u305f\u521d\u671f\u306e\u9032\u6b69\u3092\u7d39\u4ecb\u3057\u305f\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Fri, 6 Oct 2023 22:05:15 GMT)<\/li>\n\n\n\n
  9. ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images\u00a0<\/strong>[198.4]
    \u5927\u4f1a\u306f2022\u5e7412\u670830\u65e5\u306b\u958b\u304b\u308c\u30012023\u5e743\u670824\u65e5\u306b\u9589\u5e55\u3057\u305f\u3002 \u30c8\u30e9\u30c3\u30af1\u306b\u306f35\u4eba\u306e\u53c2\u52a0\u8005\u306891\u4eba\u306e\u6709\u52b9\u306a\u5fdc\u52df\u304c\u3042\u308a\u3001\u30c8\u30e9\u30c3\u30af2\u306b\u306f15\u4eba\u306e\u53c2\u52a0\u8005\u306826\u4eba\u306e\u5fdc\u52df\u304c\u3042\u308b\u3002 \u63d0\u6848\u624b\u6cd5\u306e\u6027\u80fd\u306b\u3088\u308b\u3068, \u8907\u96d1\u306a\u30b7\u30ca\u30ea\u30aa\u3084\u30bc\u30ed\u30b7\u30e7\u30c3\u30c8\u30b7\u30ca\u30ea\u30aa\u306b\u304a\u3044\u3066, \u671f\u5f85\u3055\u308c\u308b\u60c5\u5831\u62bd\u51fa\u6027\u80fd\u306b\u306f\u307e\u3060\u5927\u304d\u306a\u30ae\u30e3\u30c3\u30d7\u304c\u3042\u308b\u3068\u8003\u3048\u3089\u308c\u308b\u3002
    \u8ad6\u6587<\/a>\u00a0\u00a0\u53c2\u8003\u8a33\uff08\u30e1\u30bf\u30c7\u30fc\u30bf\uff09<\/a>\u00a0 \u00a0(Mon, 5 Jun 2023 22:20:52 GMT)<\/li>\n\n\n\n
  10. De Novo Molecular Generation via Connection-aware Motif Mining\u00a0<\/strong>[198.0]
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