10 research outputs found
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Recognizing arbitrary multi-character text in unconstrained natural
photographs is a hard problem. In this paper, we address an equally hard
sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from
Street View imagery. Traditional approaches to solve this problem typically
separate out the localization, segmentation, and recognition steps. In this
paper we propose a unified approach that integrates these three steps via the
use of a deep convolutional neural network that operates directly on the image
pixels. We employ the DistBelief implementation of deep neural networks in
order to train large, distributed neural networks on high quality images. We
find that the performance of this approach increases with the depth of the
convolutional network, with the best performance occurring in the deepest
architecture we trained, with eleven hidden layers. We evaluate this approach
on the publicly available SVHN dataset and achieve over accuracy in
recognizing complete street numbers. We show that on a per-digit recognition
task, we improve upon the state-of-the-art, achieving accuracy. We
also evaluate this approach on an even more challenging dataset generated from
Street View imagery containing several tens of millions of street number
annotations and achieve over accuracy. To further explore the
applicability of the proposed system to broader text recognition tasks, we
apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the
most secure reverse turing tests that uses distorted text to distinguish humans
from bots. We report a accuracy on the hardest category of reCAPTCHA.
Our evaluations on both tasks indicate that at specific operating thresholds,
the performance of the proposed system is comparable to, and in some cases
exceeds, that of human operators
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
Linear Transformation with Given Eigenvectors
Drag the locators to specify two eigenvectors and their corresponding eigenvalues (taken from the length of the vectors). The image shows how a linear transformation, uniquely determined by these eigenvector/eigenvalue pairs, transforms points on the unit circleComponente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic
Multidimensional Scaling
Classical (metric) multidimensional scaling (MDS) tries to find points that have a given set of pairwise distances. When no set of points satisfies distance constraints, MDS finds the best solution in the least squares sense-sum of squared errors (SSE) is minimized. Sliders specify desired distances between the points, that is, a slider labelled 3 4gives the desired distance between points 3 and 4. The darkness of the line between each pair of points reflects how closely the actual distance meets the specified goal (lighter means better correspondence)Componente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic
Multidimensional Scaling
Classical (metric) multidimensional scaling (MDS) tries to find points that have a given set of pairwise distances. When no set of points satisfies distance constraints, MDS finds the best solution in the least squares sense-sum of squared errors (SSE) is minimized. Sliders specify desired distances between the points, that is, a slider labelled 3 4gives the desired distance between points 3 and 4. The darkness of the line between each pair of points reflects how closely the actual distance meets the specified goal (lighter means better correspondence)Componente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic
Training Conditional Random Fields via Gradient Tree Boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly in problems with large numbers of potential input features. This paper describes a new method..
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Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly in problems with large numbers of potential input features. This paper describes a new method for training CRFs by applying Friedman’s (1999) gradient tree boosting method. In tree boosting, the CRF potential functions are represented as weighted sums of regression trees. Regression trees are learned by stage-wise optimizations similar to Adaboost, but with the objective of maximizing the conditional likelihood P(Y|X) of the CRF model. By growing regression trees, interactions among features are introduced only as needed, so although the parameter space is potentially immense, the search algorithm does not explicitly consider the large space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially like previous algorithms based on iterative scaling and gradient descent.To appear, International Conference on Machine Learning, 2004
Hand Recognition Using Geometric Classifiers
Introduction Biometric recognition systems find applications in security systems with varying requirements. While finger printing and iris based systems work well for high security applications, they are not as suitable for medium and low security applications because of privacy concerns. Hand Geometry based verification systems find more acceptance because hand geometry is not considered distinctive enough to establish a positive identity. Hand geometry recognition systems may provide three kinds of services. Verification, classification and identification. For verification the user provides her identity along with the hand geometry and the system verifies her identity. For classification the user does not provide any identity information but is known to be legitimate. For identification the user does not provide any identity information other than the hand geometry and may be an intruder. The system tries to identify the individual or deny access. Previous work Jain et.al. develo
Knowns and Unknowns about CAR-T Cell Dysfunction
Immunotherapy using chimeric antigen receptor (CAR) T cells is a promising option for cancer treatment. However, T cells and CAR-T cells frequently become dysfunctional in cancer, where numerous evasion mechanisms impair antitumor immunity. Cancer frequently exploits intrinsic T cell dysfunction mechanisms that evolved for the purpose of defending against autoimmunity. T cell exhaustion is the most studied type of T cell dysfunction. It is characterized by impaired proliferation and cytokine secretion and is often misdefined solely by the expression of the inhibitory receptors. Another type of dysfunction is T cell senescence, which occurs when T cells permanently arrest their cell cycle and proliferation while retaining cytotoxic capability. The first section of this review provides a broad overview of T cell dysfunctional states, including exhaustion and senescence; the second section is focused on the impact of T cell dysfunction on the CAR-T therapeutic potential. Finally, we discuss the recent efforts to mitigate CAR-T cell exhaustion, with an emphasis on epigenetic and transcriptional modulation