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Author

Patrick Haffner

Other affiliations: Nuance Communications, Carnegie Mellon University, Orange S.A.  ...read more
Bio: Patrick Haffner is an academic researcher from AT&T Labs. The author has contributed to research in topics: Support vector machine & Speaker recognition. The author has an hindex of 32, co-authored 97 publications receiving 42604 citations. Previous affiliations of Patrick Haffner include Nuance Communications & Carnegie Mellon University.


Papers
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Journal ArticleDOI
TL;DR: A novel two-step model, which seamlessly integrates these collective traffic statistics into the existing traffic classification system is proposed, which easily scales to classify traffic on 10Gbps links and displays performance improvement on all traffic classes and an overall error rate reduction.
Abstract: The ability to accurately and scalably classify network traffic is of critical importance to a wide range of management tasks of large networks, such as tier-1 ISP networks and global enterprise networks. Guided by the practical constraints and requirements of traffic classification in large networks, in this article, we explore the design of an accurate and scalable machine learning based flow-level traffic classification system, which is trained on a dataset of flow-level data that has been annotated with application protocol labels by a packet-level classifier. Our system employs a lightweight modular architecture, which combines a series of simple linear binary classifiers, each of which can be efficiently implemented and trained on vast amounts of flow data in parallel, and embraces three key innovative mechanisms, weighted threshold sampling, logistic calibration, and intelligent data partitioning, to achieve scalability while attaining high accuracy. Evaluations using real traffic data from multiple locations in a large ISP show that our system accurately reproduces the labels of the packet level classifier when runs on (unlabeled) flow records, while meeting the scalability and stability requirements of large ISP networks. Using training and test datasets that are two months apart and collected from two different locations, the flow error rates are only 3p for TCP flows and 0.4p for UDP flows. We further show that such error rates can be reduced by combining the information of spatial distributions of flows, or collective traffic statistics, during classification. We propose a novel two-step model, which seamlessly integrates these collective traffic statistics into the existing traffic classification system. Experimental results display performance improvement on all traffic classes and an overall error rate reduction by 15p. In addition to a high accuracy, at runtime, our implementation easily scales to classify traffic on 10Gbps links.

90 citations

Patent
Subhabrata Sen1, Nick Duffield1, Patrick Haffner1, Jeffrey Erman1, Yu Jin1 
11 Aug 2009
TL;DR: In this paper, a traffic classifier has a plurality of binary classifiers, each associated with one of the plurality of calibrators, each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve.
Abstract: A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

78 citations

Patent
05 May 2011
TL;DR: In this article, a system is configured to verify a speaker, generates a text challenge that is unique to the request, and prompts the speaker to utter the text challenge, and then the system records a dynamic image feature of the speaker as the speaker utters the challenge.
Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for performing speaker verification. A system configured to practice the method receives a request to verify a speaker, generates a text challenge that is unique to the request, and, in response to the request, prompts the speaker to utter the text challenge. Then the system records a dynamic image feature of the speaker as the speaker utters the text challenge, and performs speaker verification based on the dynamic image feature and the text challenge. Recording the dynamic image feature of the speaker can include recording video of the speaker while speaking the text challenge. The dynamic feature can include a movement pattern of head, lips, mouth, eyes, and/or eyebrows of the speaker. The dynamic image feature can relate to phonetic content of the speaker speaking the challenge, speech prosody, and the speaker's facial expression responding to content of the challenge.

71 citations

Patent
28 Apr 2008
TL;DR: In this paper, a method for creating a personal animated entity for delivering a multi-media message from a sender to a recipient is described. Butler et al. present a method to create a personal animator for multi-modal messages.
Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

65 citations

Proceedings Article
06 Aug 2007
TL;DR: It is demonstrated that the history and the structure of the IP addresses can reduce the adverse impact of mail server overload, by increasing the number of legitimate e-mails accepted by a factor of 3.
Abstract: E-mail has become indispensable in today's networked society. However, the huge and ever-growing volume of spam has become a serious threat to this important communication medium. It not only affects e-mail recipients, but also causes a significant overload to mail servers which handle the e-mail transmission. We perform an extensive analysis of IP addresses and IP aggregates given by network-aware clusters in order to investigate properties that can distinguish the bulk of the legitimate mail and spam. Our analysis indicates that the bulk of the legitimate mail comes from long-lived IP addresses. We also find that the bulk of the spam comes from network clusters that are relatively long-lived. Our analysis suggests that network-aware clusters may provide a good aggregation scheme for exploiting the history and structure of IP addresses. We then consider the implications of this analysis for prioritizing legitimate mail. We focus on the situation when mail server is overloaded, and the goal is to maximize the legitimate mail that it accepts. We demonstrate that the history and the structure of the IP addresses can reduce the adverse impact of mail server overload, by increasing the number of legitimate e-mails accepted by a factor of 3.

64 citations


Cited by
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Journal ArticleDOI
28 May 2015-Nature
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

46,982 citations

Journal ArticleDOI
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

42,067 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

40,257 citations

Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

40,147 citations

Journal ArticleDOI
08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

38,211 citations