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31 - 80 of 87 search results for postgraduate entry requirements |u:mi.eng.cam.ac.uk where 2 match all words and 85 match some words.
  1. Results that match 2 of 3 words

  2. Bitext Alignment forStatistical Machine Translation Yonggang Deng A…

    mi.eng.cam.ac.uk/~wjb31/ppubs/YDengDissertationDec05.pdf
    16 Feb 2008: This example presents the target sentence as asequence of phrases, but there is no requirement that the target wordsgenerated by a source word should appear as neighbors in the targetsentence. ... The precisionof each of these is plotted versus its
  3. 22 Nov 2006: Although, for many tasks, enu-merating all possible sub-strings yields a very high di-mensional feature-space, this feature-space is sparse withnon-zero entries being calculated using efficient dynamic. ... Viterbi de-coding yields a single sequence (the
  4. Semi-Supervised Video Segmentation using Tree Structured Graphical…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2011-CVPR-video-segmentation.pdf
    13 Mar 2018: Existing video models [7, 10, 21, 9] donot satisfy one or more of these requirements as discussedin Sec.2. ... 0.9 along the diagonals and equalvalues along the non-diagonal elements such that the sum ofall entries is unity.
  5. 17 Nov 2021: D(can)w Dictionary entry containing canonical pronunciations (i.e. phone sequences) for a.
  6. Non-rigid Photometric Stereo with Colored Lights Carlos Herńandez1…

    mi.eng.cam.ac.uk/research/projects/VideoNormals/NonRigidPhotometricStereoWithColoredLights_iccv2007.pdf
    16 Aug 2007: In thepresent work, we avoid this requirement while producingdetailed results. Pilet et al. ... The main difficultywith applying photometric stereo to deforming objects liesin the requirement of changing the light source direction foreach captured frame,
  7. Multiview Geometry: Profiles and Self-Calibration Paulo Ricardo dos…

    mi.eng.cam.ac.uk/reports/svr-ftp/mendonca_phd-thesis.pdf
    22 May 2001: plane. Degrees of Freedom of". A generic matrix has nine degrees of freedom(dof), one for each of its entries. ... cameras$. and$. , denoted as and , respectively, each correspondence provides, when substituted in (3.5), one equation on the entries of. ".
  8. 20 Feb 2018: generalisation properties for generating unseen inputs. Section 3 describes BAGEL’s. meaning representation, which satisfies both requirements. ... intractable for non-trivial domains. Since a key requirement of this work was to develop.
  9. Semi-Supervised Video Segmentation using Tree Structured Graphical…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2011-CVPR-video-segmentation.pdf
    13 Mar 2018: Existing video models [7, 10, 21, 9] donot satisfy one or more of these requirements as discussedin Sec.2. ... 0.9 along the diagonals and equalvalues along the non-diagonal elements such that the sum ofall entries is unity.
  10. 9 Aug 2005: Both their large size (and consequent memory requirements),and the training set sparseness associated with large numbers of parameters, limits n to 2, 3 or perhaps4. ... from the symmetry requirement, we may write:. Probability in of any event occurring
  11. PROC IEEE, VOL. 101, NO. 5, 1160-1179, 2013 1 ...

    mi.eng.cam.ac.uk/~sjy/papers/ygtw13.pdf
    20 Feb 2018: the dialogue and the attribute values (often called slots) thatdetermine the user’s requirements. ... eachdiscrete codebook entry b̂i.
  12. tech.dvi

    mi.eng.cam.ac.uk/~mjfg/liao_tr552.pdf
    21 Sep 2007: While this provides an idealised method to evaluate this compensation formseparate from the noise estimation process, this is not a requirement. ... Hence, the newestimate needs to be backed off towards the old until this requirement is met.
  13. Minimum Bayes-Risk Techniquesin Automatic Speech Recognition and…

    mi.eng.cam.ac.uk/~wjb31/ppubs/ShankarKumarDiss04.pdf
    16 Feb 2008: 82. 4.2 Distribution of the number of words in the target and the sourcephrases over the Phrase-Pair Inventory on the French-English Task.The entries are phrase-pair counts ... 83. 4.3 Distribution of the number of words in the target and the
  14. sig-004.dvi

    mi.eng.cam.ac.uk/~sjy/papers/gayo07.pdf
    20 Feb 2018: Foundations and Trends R inSignal ProcessingVol. 1, No. 3 (2007) 195–304c 2008 M. Gales and S. YoungDOI: 10.1561/2000000004. The Application of Hidden Markov Modelsin Speech Recognition. Mark Gales1 and Steve Young2. 1 Cambridge University
  15. A New Distance for Scale-Invariant 3D Shape Recognition and ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2011-ICCV-3D-object-recognition.pdf
    13 Mar 2018: 10] quantize translations and rotations in twoseparate 3D arrays; peak entries in both arrays indicate thepose of the object, but multiple objects create ambiguities.Knopp et al. ... This is not practical using Hough-based approaches,due to their
  16. BUDVYTIS et al.: LABEL PROPAGATION 1 Label propagation in ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2010-BMVC-label-propagation.pdf
    13 Mar 2018: Parts around the vanishing point and entry points of moving objects into the scene, suchas cross roads. ... Computational requirements The E-step in algorithm2 was implemented in C# with a 8core processor at a cost of 90s/frame.
  17. sig-004.dvi

    mi.eng.cam.ac.uk/~mjfg/mjfg_NOW.pdf
    19 Mar 2008: Foundations and Trends R inSignal ProcessingVol. 1, No. 3 (2007) 195–304c 2008 M. Gales and S. YoungDOI: 10.1561/2000000004. The Application of Hidden Markov Modelsin Speech Recognition. Mark Gales1 and Steve Young2. 1 Cambridge University
  18. 11 Jul 2017: Joint Training Methods for Tandem andHybrid Speech Recognition Systems. using Deep Neural Networks. Chao Zhang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofDoctor of Philosophy. Peterhouse July
  19. Refining Architectures of Deep Convolutional Neural Networks Sukrit…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2016-CVPR-refining-CNN.pdf
    13 Mar 2018: Entry of (1,1) implies no stretchand splitting should be done. In Fig 5, for DR-1 , every value of b is made 1, while for DR-2 , every value of ... Ifthat test image has say 5 positive labels in ground-truth an-notations, we expect the first 5 entries of
  20. PhD Thesis

    mi.eng.cam.ac.uk/~mjfg/thesis_ky219.pdf
    16 Nov 2007: writing. The firm requirements and countless guidance on these aspects have given me the.
  21. 20 Feb 2018: As indicated in the preceding section, there are strong indicators that humans exploit similar mechanisms and overall, POMDPs appear to address all of the requirements listed in the introduction for an
  22. Noname manuscript No.(will be inserted by the editor) Distances ...

    mi.eng.cam.ac.uk/~cipolla/publications/article/2014-IJCV-Pham.pdf
    13 Mar 2018: Divergences, also sometimes referred to as qua-sisemimetrics, form a further superset of metrics whichalso drop the requirement of sub-additivity, requiringonly conditions 1 and 2.
  23. thesis.dvi

    mi.eng.cam.ac.uk/~wjb31/ppubs/VenkataramaniDiss05.pdf
    16 Feb 2008: requirements for the degree of Doctor of Philosophy. Baltimore, Maryland. 2005. ... given by a dictionary. A word can have more than one entry in a dictionary with a.
  24. 5 Jun 2006: MODEL-BASED TECHNIQUES FORNOISE ROBUST SPEECH RECOGNITION. Mark John Francis Gales. Gonville and Caius College. September 1995. Dissertation submitted to the University of Cambridgefor the degree of Doctor of Philosophy. Declaration. This
  25. Improving Attention-based Sequence-to-sequence Models

    mi.eng.cam.ac.uk/~mjfg/thesis_qd212.pdf
    5 Jul 2022: The weights, i.e. the entries of an attention vector,indicate the focus on the input tokens.
  26. This article appeared in a journal published by Elsevier. ...

    mi.eng.cam.ac.uk/~cipolla/publications/article/2009-CVIU-face-illumination.pdf
    13 Mar 2018: training data requirements, promising resultsare reported by the more recent, generalized photometric stereomethods [13,14,16] which in addition exploit class-specific con-straints of face shape and albedo. ... Fig. 6. Our offline algorithm implicitly
  27. Semantic Transform: Weakly Supervised Semantic Inference for Relating …

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2013-ICCV-Shankar-attrirbutes.pdf
    13 Mar 2018: 3Note that the main requirement is that the classes are ordered so asto correctly reflect the underlying attribute-specific themes and the learntmodel maximally separates the classes while conforming to the ... all training images forattribute am, and
  28. 27 Oct 2015: virtually unlimited vocabulary size is a requirement. Besides vocabulary, the difficulty of these. ... Note that because of the use of the entry state, state 1, a1j specifies the initial state.
  29. Statistical Machine Translationand Automatic Speech Recognitionunder…

    mi.eng.cam.ac.uk/~wjb31/ppubs/LMathiasDissDec07.pdf
    16 Feb 2008: system can be used without modification to translate each entry, and the resulting. ... To restrict the memory requirements of the model, we extract only the phrase.
  30. 2 Apr 2014: Discriminative models for speech. recognition. Anton Ragni. Peterhouse. University of Cambridge. A thesis submitted for the degree of. Doctor of Philosophy. 2013. Declaration. This dissertation is the result of my own work and includes nothing.
  31. 16 Jan 2008: independent given the previous state. Upon entry to a state θt =j, a single observation, ot,.
  32. A New Distance for Scale-Invariant 3D Shape Recognition and ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2011-ICCV-3D-object-recognition.pdf
    13 Mar 2018: 10] quantize translations and rotations in twoseparate 3D arrays; peak entries in both arrays indicate thepose of the object, but multiple objects create ambiguities.Knopp et al. ... This is not practical using Hough-based approaches,due to their
  33. BUDVYTIS et al.: LABEL PROPAGATION 1 Label propagation in ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2010-BMVC-label-propagation.pdf
    13 Mar 2018: Parts around the vanishing point and entry points of moving objects into the scene, suchas cross roads. ... Computational requirements The E-step in algorithm2 was implemented in C# with a 8core processor at a cost of 90s/frame.
  34. 25 Feb 2010: Kernel Methods forText-Independent. Speaker Verification. Chris Longworth. Cambridge University Engineering Departmentand. Christ’s CollegeFebruary 23, 2010. Dissertation submitted to the University of Cambridgefor the degree of Doctor of
  35. Department of Engineering 1 E�cient decodingwith continuous rational…

    mi.eng.cam.ac.uk/~mjfg/Kernel/van_dalen-2012-tr-efficient_score-spaces.pdf
    27 Mar 2013: is requirement restricts the form of the kernel. e alternative,which this paper will use, is to work directly on the primal representation of the kernel.is means that the kernel ... φ(Osi,wi), (3). where Osi indicates the observations in segment si. In
  36. Int J Comput VisDOI 10.1007/s11263-012-0563-2 A Performance…

    mi.eng.cam.ac.uk/~cipolla/publications/article/2012-IJCV-3D-interestpoints.pdf
    13 Mar 2018: The proposed combinedscore is computed based on repeatability ratio with respectto varying accuracy requirements. ... For each detector, the detected. Table 3 For each entry, top to bottom: The average number of interestpoints detected, the average
  37. Semantic Transform: Weakly Supervised Semantic Inference for Relating …

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2013-ICCV-Shankar-attrirbutes.pdf
    13 Mar 2018: 3Note that the main requirement is that the classes are ordered so asto correctly reflect the underlying attribute-specific themes and the learntmodel maximally separates the classes while conforming to the ... all training images forattribute am, and
  38. Noname manuscript No.(will be inserted by the editor) Distances ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2014-IJCV-Pham.pdf
    13 Mar 2018: Divergences, also sometimes referred to as qua-sisemimetrics, form a further superset of metrics whichalso drop the requirement of sub-additivity, requiringonly conditions 1 and 2.
  39. 16 Nov 2007: i=2 αi(τ )aij j = Ns, τ = T. (2.12). where Ns is the number of states in each HMM, including the non-emitting entry and exit states. ... moments to be stored as full matrices for each component. Again the computational requirement.
  40. This article appeared in a journal published by Elsevier. ...

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2009-CVIU-face-illumination.pdf
    13 Mar 2018: training data requirements, promising resultsare reported by the more recent, generalized photometric stereomethods [13,14,16] which in addition exploit class-specific con-straints of face shape and albedo. ... Fig. 6. Our offline algorithm implicitly
  41. newsletter.indd

    mi.eng.cam.ac.uk/~cipolla/archive/Public-Understanding/2005-Insight-Tracking-Crowds.pdf
    7 Nov 2014: Hand in hand with this development, and working in close collaboration with the Cambridge Computational Biology Institute (CCBI), the aim is also to increase the breadth and capacity of our postgraduate ... There are clear benefi ts for both academic and
  42. 16 Nov 2007: The diagram, adopted from. the hidden Markov model toolkit (HTK) [129], has non-emitting entry and exit states, and. ... The transitions from the non-emitting entry state are re-. estimated by â1j = γj(1) for 1 < j < Ns and the transitions from the
  43. 9 Jul 2008: varying signals [8], and has since formed the basis of many ASR systems.An HMM is a finite state machine, where the entry to each state has an associated outputdistribution,
  44. The State Based Mixture of Experts HMM with Applications ...

    mi.eng.cam.ac.uk/reports/svr-ftp/tuerk_thesis.pdf
    2 Feb 2002: The State Based Mixture of Experts HMM. with Applications to the. Recognition of Spontaneous Speech. Andreas Tuerk. Emmanuel College. and. Cambridge University Engineering Department. September 2001. Dissertation submitted to the University of
  45. Lattice Rescoring Methods forStatistical Machine Translation Graeme…

    mi.eng.cam.ac.uk/~wjb31/ppubs/gwbthesis2010.pdf
    6 Oct 2010: Lattice Rescoring Methods forStatistical Machine Translation. Graeme Blackwood. Cambridge University Engineering Departmentand. Clare College. Dissertation submitted to the University of Cambridgefor the degree of Doctor of Philosophy.
  46. 17 Sep 2008: Uncertainty Decoding forNoise Robust Speech Recognition. Hank Liao. Sidney Sussex CollegeUniversity of Cambridge. September 2007. This dissertation is submitted for the degree ofDoctor of Philosophy to the University of Cambridge. Declaration. This
  47. Int J Comput VisDOI 10.1007/s11263-012-0563-2 A Performance…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2012-IJCV-3D-interestpoints.pdf
    13 Mar 2018: The proposed combinedscore is computed based on repeatability ratio with respectto varying accuracy requirements. ... For each detector, the detected. Table 3 For each entry, top to bottom: The average number of interestpoints detected, the average
  48. PROBABILISTIC ACOUSTIC MODELLING FOR PARAMETRIC SPEECH SYNTHESIS Sean …

    mi.eng.cam.ac.uk/~wjb31/ppubs/shannon2014probabilistic-thesis.pdf
    2 Feb 2015: Here the parameter corresponding to y 7 yd is theentry bd of the b-value b, and the parameter corresponding to y 712ydye is the entry Pdeof the precision matrix P.
  49. Linear Gaussian Models for Speech Recognition

    mi.eng.cam.ac.uk/reports/svr-ftp/rosti_thesis.pdf
    8 Oct 2004: The diagram, adopted from. the hidden Markov model toolkit (HTK) [130], has non-emitting entry and exit states, and. ... The transitions from the non-emitting entry state are re-. estimated by â1j = γj(1) for 1 < j < Ns and the transitions from the
  50. Department of Engineering 1 Generative Kernels and Score-Spaces…

    mi.eng.cam.ac.uk/~mjfg/Kernel/rcv25_2013_y2.pdf
    9 Sep 2013: Requirements on the typesof weights that can be used in weights automata are well-established (Mohri 2009). ... Formany algorithms, including the forward algorithm, the requirement is that the weightsare in a semiring.
  51. 9 Aug 2005: The size of the tentative-list is of prime practical importance during first-pass processing, since each entry requires significantlymore storage than in the fixed-list. ... $% &(' () %%-,. / 0 %h11of the model has low memory requirements, the technique

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