106 research outputs found
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
This paper proposes a novel approach for relation extraction from free text
which is trained to jointly use information from the text and from existing
knowledge. Our model is based on two scoring functions that operate by learning
low-dimensional embeddings of words and of entities and relationships from a
knowledge base. We empirically show on New York Times articles aligned with
Freebase relations that our approach is able to efficiently use the extra
information provided by a large subset of Freebase data (4M entities, 23k
relationships) to improve over existing methods that rely on text features
alone
Implementing Fairness Constraints in Markets Using Taxes and Subsidies
Fisher markets are those where buyers with budgets compete for scarce items,
a natural model for many real world markets including online advertising. A
market equilibrium is a set of prices and allocations of items such that supply
meets demand. We show how market designers can use taxes or subsidies in Fisher
markets to ensure that market equilibrium outcomes fall within certain
constraints. We show how these taxes and subsidies can be computed even in an
online setting where the market designer does not have access to private
valuations. We adapt various types of fairness constraints proposed in existing
literature to the market case and show who benefits and who loses from these
constraints, as well as the extent to which properties of markets including
Pareto optimality, envy-freeness, and incentive compatibility are preserved. We
find that some prior discussed constraints have few guarantees in terms of who
is made better or worse off by their imposition
Fader Networks: Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying the attribute values. By using continuous attribute values, we can
choose how much a specific attribute is perceivable in the generated image.
This property could allow for applications where users can modify an image
using sliding knobs, like faders on a mixing console, to change the facial
expression of a portrait, or to update the color of some objects. Compared to
the state-of-the-art which mostly relies on training adversarial networks in
pixel space by altering attribute values at train time, our approach results in
much simpler training schemes and nicely scales to multiple attributes. We
present evidence that our model can significantly change the perceived value of
the attributes while preserving the naturalness of images.Comment: NIPS 201
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