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Efficient Large-Scale Semantic VisualLocalization in 2D Maps Tomas…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2020-ACCV-Large-scale-localization.pdf28 Jun 2021: database of embeddings of the test environmentlocations to retrieve the most similar location. ... The plots in the middle show mean confidence of trajectories w.r.t. -
Contour-Based Learning for Object Detection Jamie ShottonDepartment…
mi.eng.cam.ac.uk/reports/svr-ftp/shotton_iccv05.pdf8 Aug 2005: Test-ing was performed on 164 images containing 193 cars,and164 background images. ... Our technique relies on edge features andhigher-resolution training and test images would certainlyimprove results. -
Inverse Reinforcement Learning for Micro-Turn Management Dongho Kim,…
mi.eng.cam.ac.uk/~sjy/papers/kgbt14.pdf20 Feb 2018: The environment T in a training dia-logue might be different from that of the test dialogues, and thuswe computed a maximum-entropy randomised policy in the testenvironments given the learnt ... In future work, theIRL reward function will be integrated -
This article appeared in a journal published by Elsevier. ...
mi.eng.cam.ac.uk/~sjy/papers/inyo0920 Feb 2018: Table 3Percent of unseen contexts in the test data. Number of matching features. ... Noduration modification was applied for this test. Twenty subjects participated in the evaluation. -
paper.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/kim_icslp04.pdf10 Jan 2005: The transcription is either known in training orobtained by an initial, non-VTLN, decoding pass for test data.If and are the original and transformed feature vectorsrespectively then the log-likelihood ... The systems were eval-uated on two 3 hour test -
Optimisation of Fast LVCSR Systems Gunnar Evermann, Phil Woodland ...
mi.eng.cam.ac.uk/research/projects/EARS/pubs/evermann_stthomas03.pdf10 Dec 2003: P1 speed-accuracy trade-off (CTS eval02). • In eval chose middle operating point for safety Should have used fast setup and use time elsewhere. ... test on eval03: skip 66% segments, 43% audio, 32% rescoring runtimei.e. -
IMPLEMENTATION OF AUTOMATIC CAPITALISATIONGENERATION SYSTEMS FOR…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_icassp02.pdf9 Aug 2005: We also used 3 hours of test data from the NIST 1998 Hub-4BN benchmark tests. ... Most of these words are not capitalised,if they are used in the middle of sentences. -
Towards Automatic Assessment of Spontaneous Spoken English Y.…
mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ALTA_SpComm2017.pdf12 Sep 2018: computed over all the test sectionswhere the candidate is required to produce spontaneousspeech. ... xN}, what is the best estimate of thevalue of the function at test point x. -
Ghostscript wrapper for…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-CVPR-Kim-incremental.pdf13 Mar 2018: test data. The descriptor should also be compact, even for. large data sets. ... divided into training and test sets. All basis vectors were. extracted from the training set. -
Pronunciation modeling by sharing Gaussians
mi.eng.cam.ac.uk/reports/svr-ftp/nock_csl00.pdf31 Jul 2000: Pronunciation modeling by sharing Gaussians 141. TABLE I. WER degradation with speaking style on theMULTI-REG test set. ... Steps (6), (3), (4) and (5) arethen carried out in that order to estimate and test a matched pronunciation model.
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