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.

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

Example Input: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.
Example Output: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Example Input: <lb>An associative memory is a framework of content-addressable memory that stores a collection of<lb>message vectors (or a dataset) over a neural network while enabling a neurally feasible mechanism to<lb>recover any message in the dataset from its noisy version. Designing an associative memory requires<lb>addressing two main tasks: 1) learning phase: given a dataset, learn a concise representation of the<lb>dataset in the form of a graphical model (or a neural network), 2) recall phase: given a noisy version of<lb>a message vector from the dataset, output the correct message vector via a neurally feasible algorithm<lb>over the network learnt during the learning phase. This paper studies the problem of designing a class<lb>of neural associative memories which learns a network representation for a large dataset that ensures<lb>correction against a large number of adversarial errors during the recall phase. Specifically, the associa-<lb>tive memories designed in this paper can store dataset containing exp(n) n-length message vectors over<lb>a network with O(n) nodes and can tolerate Ω( n<lb>polylogn ) adversarial errors. This paper carries out this<lb>memory design by mapping the learning phase and recall phase to the tasks of dictionary learning with<lb>a square dictionary and iterative error correction in an expander code, respectively.
Example Output:
Associative Memory using Dictionary Learning and Expander Decoding