GitHub - milvus-io/milvus: Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
🦠Milvus is a high-performance vector database built for scale. It powers AI applications by efficiently organizing and searching vast amounts of unstructured data, such as text, images, and multi-modal information. 🧑â€ðŸ’» Written in Go and C++, Milvus implements hardware acceleration for CPU/GPU to achieve best-in-class vector search performance. Thanks to its fully-distributed and K8s-native arc
GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.
Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity,
Taming data | MIT CSAIL
The age of big data has seen a host of new techniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up. That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizin
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高次元データã®å¤–れ値検出ã«ã¤ã„ã¦ã®ãƒ¡ãƒ¢ï¼Ž 高次元データã¨æ¬¡å…ƒã®å‘ªã„ 次元ãŒå¤§ãããªã‚‹ã»ã©ï¼Œç‚¹ã®é–“ã®è·é›¢ã¯å‡ä¸€ã«ãªã£ã¦ã„ã． 例ã¨ã—ã¦ï¼Œ2000個ã®ç‚¹ã®å„座標を一様乱数ã§ç™ºç”Ÿã•ã›ã¦ï¼Œæ¬¡å…ƒã‚’変ãˆãªãŒã‚‰ç‚¹ã®é–“ã®è·é›¢ã®å¹³å‡å€¤ï¼Œæœ€å¤§å€¤ï¼Œæœ€å°å€¤ï¼Œå¹³å‡å€¤Â±1σ，平å‡å€¤Â±2σをã¿ã¦ã¿ã‚ˆã†ï¼Ž library(ggplot2) set.seed(123) # 次元ã®ãƒªã‚¹ãƒˆ dims <- c(1:9, 10*(1:9), 100*(1:10)) # 算出ã™ã‚‹çµ±è¨ˆé‡ stats <- c("min", "mean-sd", "mean", "mean+sd", "max") # 発生ã•ã›ã‚‹ç‚¹ã®å€‹æ•° N <- 2000 # å„次元ã«å¯¾ã—ã¦ç®—出ã—ãŸçµ±è¨ˆé‡ã‚’æ ¼ç´ã™ã‚‹è¡Œåˆ— ans <- matrix(NA, length(dims), length(stats), dimnames=list(dims, stats))
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パターンマイニングã¯ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã‚’代表ã™ã‚‹æ‰‹æ³•ã®ä¸€ã¤ã§ï¼Œç‰¹ã«ã‚¢ã‚½ã‚·ã‚¨ãƒ¼ã‚·ãƒ§ãƒ³ãƒ«ãƒ¼ãƒ«ã‚’é©ç”¨ã—ãŸã€Œãƒ“ールã¨ãŠã‚€ã¤ã€ãªã©ã®ä¾‹ãŒæœ‰åã§ã™ï¼Ž 最近ã¯ï¼ŒRãªã©ã®ãƒ‡ãƒ¼ã‚¿åˆ†æžãƒ„ールã§ã‚‚Aprioriã‚„Eclat(é »å‡ºãƒ‘ã‚¿ãƒ¼ãƒ³ãƒžã‚¤ãƒ‹ãƒ³ã‚°), CSPADE(系列パターンマイニング)ç‰ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を実行ã™ã‚‹ãƒ©ã‚¤ãƒ–ラリãŒæä¾›ã•ã‚Œã¦ãŠã‚Šï¼Œãƒ‘ターンマイニングを実行ã™ã‚‹ã“ã¨ã®éšœå£ã¯æ¯”較的低ããªã£ã¦ã„ã¾ã™ï¼Ž パターンマイニングã§ã¯ï¼Œä¸€èˆ¬çš„ã«è†¨å¤§ãªæ•°ã®ãƒ‘ターンãŒæŠ½å‡ºã•ã‚Œã¾ã™ï¼Žã“ã®äº‹è±¡ã¯ã‚¢ã‚¤ãƒ†ãƒ ã®çµ„ã¿åˆã‚ã›ã‚„é †åˆ—ã®æ•°ãŒè†¨å¤§ã«ãªã‚‹ã“ã¨ã«èµ·å› ã—ã¦ãŠã‚Šï¼Œå°‘é‡ã®ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³ã‹ã‚‰å¤§é‡ã®ãƒ‘ターンãŒæŠ½å‡ºã•ã‚Œã‚‹ã“ã¨ã‚‚決ã—ã¦çã—ãã‚ã‚Šã¾ã›ã‚“*1．ã“ã®ã‚ˆã†ãªèƒŒæ™¯ã®ä¸‹ï¼Œãƒ‘ターンマイニングã§æŠ½å‡ºã•ã‚ŒãŸãƒ‘ターンã‹ã‚‰é‡è¦ãªãƒ‘ターンを抽出ã™ã‚‹ã“ã¨ã¯ï¼Œå¤§ããªæŠ€è¡“的課題ã®ä¸€ã¤ã ã¨è¨€ãˆã‚‹ã§ã—ょã†ï¼Ž 抽出ã—ãŸãƒ‘ターンã¯è†¨å¤§ãªæ•°ã« 以上ã§èª¬æ˜Žã—ãŸã“ã¨ã‚’実
今年ã®SIGKDDベストペーパーを実装・公開ã—ã¦ã¿ã¾ã—㟠- Preferred Networks Tech Blog
毎日暑ã„ã§ã™ã。比戸ã§ã™ã€‚ ã¡ã‚‡ã†ã©ä»Šé€±ã‚·ã‚«ã‚´ã§é–‹ã‹ã‚Œã¦ã„ãŸSIGKDD2013ã§Best research paperã«é¸ã°ã‚ŒãŸEdo Libertyæ° (Yahoo! Haifa Labs)ã®â€Simple and Deterministic Matrix Sketchingâ€ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を実装ã—ã¦å…¬é–‹ã—ã¦ã¿ã¾ã—ãŸã€‚ 元論文PDFã¯è‘—者サイトã‹ã‚‰ã€ç§ãŒæ›¸ã„ãŸPythonコードã¯Githubã‹ã‚‰ãã‚Œãžã‚Œå…¥æ‰‹ã§ãã¾ã™ã€‚ SIGKDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining)ã¯ACM主催ã§è¡Œã‚れるã€çŸ¥è˜ç™ºè¦‹ï¼†ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã«ãŠã‘るトップ会è°ã§ã™ã€‚最近ã¯æ©Ÿæ¢°å¦ç¿’ã¨ã®å¢ƒç›®ãŒæ›–昧ã«ãªã£ã¦ãã¾ã—ãŸãŒã€æŸ»èªæ™‚ã«ã¯ç†è«–çš„ãªæ–°ã—ã•ã ã‘ã§ãªãã€å®Ÿãƒ‡ãƒ¼ã‚¿ï¼ˆç‰¹ã«å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ï¼‰ã‚’使ã£ãŸå®Ÿé¨“ã§ã®è©•ä¾¡ãŒå¿…è¦ã¨ã•ã‚Œã‚‹ã®ãŒç‰¹å¾´ã§ã™ã€‚
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About Hewlett Packard Enterprise: Information and Strategic Vision
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