interleaving experiments affected by caption bias.

Abstract not found

the true interleaved presentation order of the tasks and making it difficult to distinguish learning behavior from response preferences that may represent biases. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral

for the entire learning-to-rank dataset and used weighted sum of commercial and topical relevance instead of default relevance labels. For evaluating our method we created new DCG-like metrics and conducted off-line evaluation as well as on-line interleaving experiments demonstrating that a ranking algorithm

Abstract-This successive approximation register ADC uses time-interleaving to gain the energy advantage of slower circuits (reduced supply voltage and improved bias points) without sacrificing high speed operation. The drawbacks of interleaving are addressed through architectural solutions

In this paper, we present our experiments on lightly supervised discriminative training with large amounts of broadcast news data for which only closed caption transcriptions are available (TDT data). In particular, we use language models biased to the closedcaption transcripts to recognise

and attended motion directions. We hypothesized that each voxel may have a weak but true bias in direction selectivity, and therefore the pooled output of many voxels might provide robust direction information. Such biases might arise from random variations in the distribution or response strength of neurons

to detect small differences between rankers and can be applied to learn ranking functions online. In this paper, we analyze three existing interleaved comparison methods and find that they are all either biased or insensitive to some differences between rankers. To address these problems, we present a new

approximation accuracy and selection changes when periods of local and global search are interleaved. Special attention is paid to the low overhead of the local gradient descent. Finally, the inductive bias of local learning is quantified.

’s title and cluster titles. To reduce the noise, we also filter these results and only consider those clicked images that are also retrieved by a text-based approach that uses the image captions. To promote diversity, we interleave the images retrieved in the previous step for each of the cluster