Arrow Research search

Author name cluster

Michael Perrone

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
1 author row

Possible papers

4

AAAI Conference 2020 System Paper

Causal Knowledge Extraction through Large-Scale Text Mining

  • Oktie Hassanzadeh
  • Debarun Bhattacharjya
  • Mark Feblowitz
  • Kavitha Srinivas
  • Michael Perrone
  • Shirin Sohrabi
  • Michael Katz

In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.

PDF Details

IJCAI Conference 2019 Conference Paper

Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts

  • Oktie Hassanzadeh
  • Debarun Bhattacharjya
  • Mark Feblowitz
  • Kavitha Srinivas
  • Michael Perrone
  • Shirin Sohrabi
  • Michael Katz

In this paper, we study the problem of answering questions of type "Could X cause Y? " where X and Y are general phrases without any constraints. Answering such questions will assist with various decision analysis tasks such as verifying and extending presumed causal associations used for decision making. Our goal is to analyze the ability of an AI agent built using state-of-the-art unsupervised methods in answering causal questions derived from collections of cause-effect pairs from human experts. We focus only on unsupervised and weakly supervised methods due to the difficulty of creating a large enough training set with a reasonable quality and coverage. The methods we examine rely on a large corpus of text derived from news articles, and include methods ranging from large-scale application of classic NLP techniques and statistical analysis to the use of neural network based phrase embeddings and state-of-the-art neural language models.

PDF Details

NeurIPS Conference 1994 Conference Paper

The Ni1000: High Speed Parallel VLSI for Implementing Multilayer Perceptrons

  • Michael Perrone
  • Leon Cooper

In this paper we present a new version of the standard multilayer perceptron (MLP) algorithm for the state-of-the-art in neural net(cid: 173) work VLSI implementations: the Intel Ni1000. This new version of the MLP uses a fundamental property of high dimensional spaces which allows the 12-norm to be accurately approximated by the It -norm. This approach enables the standard MLP to utilize the parallel architecture of the Ni1000 to achieve on the order of 40000, 256-dimensional classifications per second. 1 The Intel NilOOO VLSI Chip The Nestor/Intel radial basis function neural chip (Ni1000) contains the equivalent of 1024 256-dimensional artificial digital neurons and can perform at least 40000 classifications per second [Sullivan, 1993]. To attain this great speed, the Ni1000 was designed to calculate "city block" distances (Le. the II-norm) and thus to avoid the large number of multiplication units that would be required to calculate Euclidean dot products in parallel. Each neuron calculates the city block distance between its stored weights and the current input: neuron activity = L IWi -: eil (1) where w, is the neuron's stored weight for the ith input and: ei is the ith input. Thus the Nil000 is ideally suited to perform both the RCE [Reillyet al. , 1982] and 748 Michael P. Perrone. Leon N. Cooper PRCE [Scofield et al. , 1987] algorithms or any of the other commonly used radial basis function (RBF) algorithms. However, dot products are central in the calcula(cid: 173) tions performed by most neural network algorithms (e. g. MLP, Cascade Correlation, etc. ). Furthermore, for high dimensional data, the dot product becomes the compu(cid: 173) tation bottleneck (i. e. most ofthe network's time is spent calculating dot products). If the dot product can not be performed in parallel there will be little advantage using the NilOOO for such algorithms. In this paper, we address this problem by showing that we can extend the NilOOO to many of the standard neural network algorithms by representing the Euclidean dot product as a function of Euclidean norms and by then using a city block norm approximation to the Euclidean norm. Section 2, introduces the approximate dot productj Section 3 describes the City Block MLP which uses the approximate dot productj and Section 4 presents ex(cid: 173) periments which demonstrate that the City Block MLP performs well on the NIST OCR data and on human face recognition data. 2 Approximate Dot Product Consider the following approximation [Perrone, 1993]: 1

PDF Details
v2026.09.13