Search

Search Funnelback University

Search powered by Funnelback
1 - 10 of 109 search results for KaKaoTalk:po03 op |u:mi.eng.cam.ac.uk where 0 match all words and 109 match some words.
  1. Results that match 1 of 2 words

  2. A HIERARCHICAL ATTENTION BASED MODEL FOR OFF-TOPIC SPONTANEOUSSPOKEN…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ASRU2017/HierarchicalAttentionBased/hierarchical-attention-based.pdf
    24 Jan 2018: The HATMalso contains an additional 200-dimensional BiLSTM prompt-searchencoder. The ATM was trained for 5 epochs with the Adam op-timizer [19], an exponentially decaying learning rate with an
  3. 20 Feb 2018: In Section 3, the grid-based ap-. proach to policy optimisation is introduced followedby a presentation of the k-nn Monte-Carlo policy op-timization in Section 4, along with an ... 5 ConclusionIn this paper, an extension to a grid-based policy
  4. POLICY COMMITTEE FOR ADAPTATION IN MULTI-DOMAIN SPOKEN…

    mi.eng.cam.ac.uk/~sjy/papers/gmsv15.pdf
    20 Feb 2018: 5]. Here, we address the problem ofdecision-making. Moving from a limited domain dialogue system that op-erates on a relatively modest ontology to an open domain. ... 5. EXPERIMENTAL SET-UP. In order to examine the ability of the proposed method to
  5. The Effect of Cognitive Load on a Statistical Dialogue ...

    mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf
    20 Feb 2018: 4.4 Conversational patterns. Given that the subjects felt the change of cognitiveload when they were talking to the system and op-erating the car simulator at the same time, we
  6. crosseval_diff-reward2b.ps

    mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf
    20 Feb 2018: The op-tions for each random decision point are reason-able in the context in which it is encountered, buta uniform distribution of outcomes might not re-flect real user behaviour. ... Many of the decisions involvedare deterministic, allowing only one
  7. Learning Domain-Independent Dialogue Policies via…

    mi.eng.cam.ac.uk/~sjy/papers/wsws15.pdf
    20 Feb 2018: The ex-perimental results show that the policy op-timised in a restaurant search domain us-ing our domain-independent representa-tions can be deployed to a laptop sale do-main,
  8. 20 Feb 2018: the Q-function estimatewe expect during the process of learning and H is a linear op-erator that captures the reward lookahead from the Q-function(see Eq.
  9. 20 Feb 2018: increases. Pop op-erations are then performed where possible, the tree is prunedand identical nodes are joined so that the number stays constantor decreases. ... Error bars indicate 99% con-fidence intervals. This demonstrates the competitiveness of the
  10. 20 Feb 2018: A comparison between the three op-tions is included in the experimental evaluation. ... whilst suffering initially.We hypothesise that the optimised SL pre-trainedparameters distributed very differently to the op-timal A2C ER parameters.
  11. robust.dvi

    mi.eng.cam.ac.uk/~sjy/papers/heyo04.pdf
    20 Feb 2018: 4.2 Log-Linear Interpolation. Log-linear interpolation has been applied to languagemodel adaptation and has been shown to be equivalentto a constrained minimum Kullback-Leibler distance op-timisation problem(Klakow,

Refine your results

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.