TASK DEFINITION: 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.
PROBLEM: We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.

SOLUTION: Collaborative Filtering with Recurrent Neural Networks

PROBLEM: This paper presents a theoretical, idealized model of the thinking process with the following characteristics: 1) the model can produce complex thought sequences and can be generalized to new inputs, 2) it can receive and maintain input information indefinitely for the generation of thoughts and later use, and 3) it supports learning while executing. The crux of the model lies within the concept of internal consistency, or the generated thoughts should always be consistent with the inputs from which they are created. Its merit, apart from the capability to generate new creative thoughts from an internal mechanism, depends on the potential to help training to generalize better. This is consequently enabled by separating input information into several parts to be handled by different processing components with a focus mechanism to fetch information for each. This modularized view with the focus binds the model with the computationally capable Turing machines. And as a final remark, this paper constructively shows that the computational complexity of the model is at least, if not surpass, that of a universal Turing machine.

SOLUTION: (Yet) Another Theoretical Model of Thinking

PROBLEM: The ability to predict which patterns are formed in brain scans when imagining a celery or an airplane, based on how these concepts as words co-occur in texts, suggests that it is possible to model mental representations based on word statistics. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from matrices made up of Wikipedia lines and Shakespeare plots, these latent semantics approximate the associative links that form concepts. However, cognition is fundamentally intertwined with action; even passively reading verbs has been shown to activate the same motor circuits as when we tap a finger or observe actual movements. If languages evolved by adapting to the brain, sensorimotor constraints linking articulatory gestures with aspects of motion might also be reflected in the statistics of word co-occurrences. To probe this hypothesis 3 × 20 emotion, face, and hand related verbs known to activate premotor areas in the brain were selected, and latent semantic analysis LSA was applied to create a weighted adjacency matrix. Hierarchically clustering the verbs and modeling their connectivity within a force directed graph, they divide into modules of mouth and hand motion, facial expressions and negative emotions. Transforming the verbs into their constituent phonemes, the corresponding consonant vowel transitions can be represented in an articulatory space defined by tongue height and formant frequencies. Here the vowels appear positioned along a front to back continuum reflecting aspects of size and intensity related to the actions described by the verbs. More forceful verbs combine plosives and sonorants with fricatives characterized by sustained turbulent airflows, while positive and negative emotional expressions tend to incorporate upor downwards shifts in formant frequencies. Suggesting, that articulatory gestures reflect parameters of size and intensity which might be retrieved from the latent semantics of action verbs.

SOLUTION:
When push comes to shove verbs literally shake due to latent semantic parameters of size and intensity