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• Hash Embeddings for Efficient Word Representations
  ...ets is low it results in collisions hence it requires us to learn the best hash function for the problem. ...ent hash functions and then train it with some parameters to find the best hash function for that particular word/token/phrase. This method has a lot of ad ...
  4 KB (646 words) - 18:44, 26 October 2017

Page text matches

• Hash Embeddings for Efficient Word Representations
  ...ets is low it results in collisions hence it requires us to learn the best hash function for the problem. ...ent hash functions and then train it with some parameters to find the best hash function for that particular word/token/phrase. This method has a lot of ad ...
  4 KB (646 words) - 18:44, 26 October 2017
• strategies for Training Large Scale Neural Network Language Models
  This paper introduces a hash-based implementation of a class based maximum entropy model, that allows t == Hash-based implementation of class-based maximum entropy model == ...
  9 KB (1,542 words) - 08:46, 30 August 2017
• hamming Distance Metric Learning
  ...while preserving some notion of similarity. This mapping, which is called hash function is parameterized by a matrix w such that:<center><math>b(x,w)=sign Two families of hash functions are chosen: '''linear transforms''' and '''multilayer neural netw ...
  10 KB (1,792 words) - 08:46, 30 August 2017
• kernelized Locality-Sensitive Hashing
  ...valued hash functions <math>h_1,h_2,...,h_b</math> to the data points. The hash functions are designed carefully so that similar data points (in terms of s A valid hash function <math>h</math> must satisfy the property ...
  17 KB (2,894 words) - 08:46, 30 August 2017
• Bag of Tricks for Efficient Text Classification
  ...ationally expensive. In order to address this problem, we introduce the '''hash trick''', aka '''feature hashing'''. ...ure vectors to indices of an array or a matrix of fixed size by applying a hash function to features. The general idea is to map feature vectors from high ...
  32 KB (5,160 words) - 21:32, 27 March 2018
• Adversarial Attacks on Copyright Detection Systems
  ...rprinting algorithms work by extracting the features of a source file as a hash and then utilizing a matching algorithm to compare that to the materials pr ...aving time of the objects in the video (Saviaga & Toxtli, 2018). The final hash consists of the entering/leaving of different objects and a unique relation ...
  23 KB (3,604 words) - 14:03, 7 December 2020
• stat441w18/Image Question Answering using CNN with Dynamic Parameter Prediction
  ...an be space-inefficient with ''k << M''. For keys in the universe ''U'', a hash function ''h'' mapps the keys to an element from the set {1...M} -- i.e. <m ...-cost hash function to randomly group connection weights into slots in the hash table, and all connections within the same slot share a single parameter va ...
  32 KB (5,284 words) - 21:03, 19 March 2018
• Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
  # Construct <math>H</math> be a perfect hash function with <math>L</math> buckets, and <math>\phi</math> which maps ''' ...
  29 KB (4,603 words) - 20:21, 6 December 2018