DetCo: Unsupervised Contrastive Learning for Object Detection

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Lecture 13: Generative Models

Supervised vs Unsupervised Learning K-means clustering This image is CC0 public domain. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 11 May 18, 2017 Unsupervised Learning ... Examples: Clustering, dimensionality reduction, feature learning, density estimation, etc. 14 Supervised vs Unsupervised Learning Supervised Learning

  Name, Supervised, Generative, Clustering, Means clustering

Unsupervised Deep Embedding for Clustering Analysis

Several variants of k-means have been proposed to address issues with higher-dimensional input spaces.De la Torre & Kanade(2006);Ye et al.(2008) perform joint dimension-ality reduction and clustering by first clustering the data with k-means and then projecting the data into a lower di-mensions where the inter-cluster variance is maximized.

  Analysis, Name, Deep, Embedding, Unsupervised, Clustering, Unsupervised deep embedding for clustering analysis

Survey of Clustering Data Mining Techniques

3.1. Probabilistic Clustering 3.2. K-Medoids Methods 3.3. K-Means Methods 4. Density-Based Partitioning 4.1. Density-Based Connectivity 4.5. Density Functions 5. Grid-Based Methods 6. Co-Occurrence of Categorical Data 7. Other Clustering Techniques 7.1. Constraint-Based Clustering 7.2. Relation to Supervised Learning 7.3.

  Name, Data, Survey, Mining, Technique, Supervised, Clustering, Survey of clustering data mining techniques

POST GRADUATE PROGRAM IN ARTIFICIAL INTELLIGENCE

Supervised learning • Linear Regression • Multiple Variable Linear Regression • Logistic Regression • Naive Bayes Classifiers • k-NN Classification • Support Vector Machines MODULE 2 Ensemble Techniques • Decision Trees • Bagging • Random Forests • Boosting MODULE 3 Unsupervised learning • K-means Clustering

  Name, Supervised, Clustering, Means clustering

MACHINE LEARNING LABORATORY MANUAL - JNIT

for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program. 9. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions.

  Name, Clustering

An Introduction to the WEKA Data Mining System - CCSU

• K-nearest neighbor (KNN, IBk) Take the class of the nearest neighbor or the majority class among K neighbors K=1 -> no K=3 -> no K=5 -> yes K=14 -> yes (Majority predictor, ZeroR) • Distance is calculated as the number of different attribute values • Euclidean distance for numeric attributes • Weighted K-nearest neighbor K=5 -> undecided

  Wake

Machine Learning Basic Concepts - edX

K-nearest neighbors Training algorithm: Add each training example (x;y) to the dataset D. x2Rd, y2f+1; 1g. Classi cation algorithm: Given an example xqto be classi ed. Suppose Nk(xq) is the set of the K-nearest neighbors of xq. ^yq= sign(X xi2Nk(xq) yi)