dzmitry bahdanau, kyunghyun cho, and yoshua bengio. 2014. neural machine translation by jointly learning to align and
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
The models proposed as of late for brain machine interpretation frequently have a place with a group of encoder-decoders and comprises of an encoder that encodes a source sentence into a fixed-length vector from which a decoder creates an interpretation.
In this paper, we guess that the utilization of a fixed-length vector is a bottleneck in working on the exhibition of this essential encoder-decoder engineering, and propose to broaden this by permitting a model to naturally (delicate )look for parts of a source sentence that are pertinent to anticipating an objective word, without shaping these parts as a hard section unequivocally.
With this new methodology, we accomplish an interpretation execution equivalent to the current cutting edge state put together framework with respect to the undertaking of English-to-French interpretation. Moreover, subjective examination uncovers that the (delicate )arrangements found by the model concur well with our instinct.
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Every 10 years with the new U.S. Census, state legislatures set about drawing the boundaries of electoral districts in their states. The majority party in the legislature typically exerts its influence to draw districts that are favorable to itself. For instance, Republicans may observe that Democrats in their state are packed into a few urban pockets, and consequently, they will try to district them into as few groups as possible to give more representation to their Republican voters. Both major political parties are guilty of partisan gerrymandering, but the GOP spends far more money on the practice and often aims to disenfranchise minority voices.
The origin of the term "gerrymandering" is actually one of my favorite historical tidbits. Elbridge Gerry, then governor of Massachusetts, passed a law in 1812 that consolidated the Federalists into a handful of districts and gave disproportionate voice to the Democratic-Republicans. A political cartoon noted the districts' resemblance of a salamander (see picture below), and called it the "gerry-mander."
Many agree that partisan gerrymandering is a distasteful aspect of our democracy. This year, there have been a flurry of court rulings, including before the U.S. Supreme Court, examining the constitutionality of different voting maps that appear to be designed to disenfranchise minorities. The New York Times has done some excellent coverage that I highly recommend.
<span>Cliff will ignore all other needs until he satisfies his most basic needs for food and shelter. According to maslow, humans will have the tendency to fulfilled what we placed higher in the hierarchy of needs first (the one that bring the most satisfaction, in cliff's case, food and shelter) before we could move to the needs on the lower hierarchy. (such as luxury goods)</span>
d) the concentration of lower-income groups in rental housing