In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting, we first derive problem-specific regret lower bounds, and analyze how these bounds scale with the dimension of the decision space. We then propose COMBUCB, algorithms that efficiently exploit the combinatorial structure of the problem, and derive finite-time upper bound on their regrets. These bounds improve over regret upper bounds of existing algorithms, and we show numerically thatCOMBUCB significantly outperforms any other algorithm. In the adversarial setting, we propose two simple algorithms, namely COMBEXP-1 and COMBEXP-2 for semi-bandit and bandit feedback, respectively. Their regrets have similar scaling as state-of-the-art algorithms, in spite of the simplicity of their implementation.

Ex Output:
Stochastic and Adversarial Combinatorial Bandits


Ex Input:
Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning rules are not known, recent results [4, 5, 6] suggest the operation of an unsupervised temporal-association-based method e.g., Foldiak’s trace rule [7]. Such methods exploit the temporal continuity of the visual world by assuming that visual experience over short timescales will tend to have invariant identity content. Thus, by associating representations of frames from nearby times, a representation that tolerates whatever transformations occurred in the video may be achieved. Many previous studies verified that such rules can work in simple situations without background clutter, but the presence of visual clutter has remained problematic for this approach. Here we show that temporal association based on large class-specific filters (templates) avoids the problem of clutter. Our system learns in an unsupervised way from natural videos gathered from the internet, and is able to perform a difficult unconstrained face recognition task on natural images: Labeled Faces in the Wild [8]. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. ar X iv :1 40 9. 38 79 v2 [ cs .C V ] 2 4 A pr 2 01 5

Ex Output:
Unsupervised learning of clutter-resistant visual representations from natural videos


Ex Input:
A Semantic Compositional Network (SCN) is developed<lb>for image captioning, in which semantic concepts (i.e., tags)<lb>are detected from the image, and the probability of each tag<lb>is used to compose the parameters in a long short-term mem-<lb>ory (LSTM) network. The SCN extends each weight matrix of<lb>the LSTM to an ensemble of tag-dependent weight matrices.<lb>The degree to which each member of the ensemble is used<lb>to generate an image caption is tied to the image-dependent<lb>probability of the corresponding tag. In addition to caption-<lb>ing images, we also extend the SCN to generate captions for<lb>video clips. We qualitatively analyze semantic composition<lb>in SCNs, and quantitatively evaluate the algorithm on three<lb>benchmark datasets: COCO, Flickr30k, and Youtube2Text.<lb>Experimental results show that the proposed method signifi-<lb>cantly outperforms prior state-of-the-art approaches, across<lb>multiple evaluation metrics.

Ex Output:

Semantic Compositional Networks for Visual Captioning