instruction:
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.
question:
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.
answer:
(Yet) Another Theoretical Model of Thinking


question:
<lb>We consider a situation in which we see samples Xn ∈ R drawn i.i.d. from some<lb>distribution with mean zero and unknown covariance A. We wish to compute the<lb>top eigenvector of A in an incremental fashion with an algorithm that maintains<lb>an estimate of the top eigenvector in O(d) space, and incrementally adjusts the<lb>estimate with each new data point that arrives. Two classical such schemes are<lb>due to Krasulina (1969) and Oja (1983). We give finite-sample convergence rates<lb>for both.
answer:
The Fast Convergence of Incremental PCA


question:
We report efforts to interpret a Long-Short Term Memory neural network trained to recognize gender and writing instructions on a set of essays from a psychological educational intervention known as a values affirmation. Adjusting the model at test time to output sequential probabilities as each new token is encountered, rather than predicting the class holistically, we query the model with carefully constructed sentences designed to test a theoretically informed hypothesis: male versus female students write in a way that reflects a greater emphasis on independence versus interdependence, respectively. The LSTM model outperforms the baseline, and the model’s predictions to our constructed test sentences suggest modest support for these hypotheses.
answer:

Interpreting Neural Networks to Understand Written Justifications in Values-Affirmation Essays