26,042 research outputs found
Do Multi-Sense Embeddings Improve Natural Language Understanding?
Learning a distinct representation for each sense of an ambiguous word could
lead to more powerful and fine-grained models of vector-space representations.
Yet while `multi-sense' methods have been proposed and tested on artificial
word-similarity tasks, we don't know if they improve real natural language
understanding tasks. In this paper we introduce a multi-sense embedding model
based on Chinese Restaurant Processes that achieves state of the art
performance on matching human word similarity judgments, and propose a
pipelined architecture for incorporating multi-sense embeddings into language
understanding.
We then test the performance of our model on part-of-speech tagging, named
entity recognition, sentiment analysis, semantic relation identification and
semantic relatedness, controlling for embedding dimensionality. We find that
multi-sense embeddings do improve performance on some tasks (part-of-speech
tagging, semantic relation identification, semantic relatedness) but not on
others (named entity recognition, various forms of sentiment analysis). We
discuss how these differences may be caused by the different role of word sense
information in each of the tasks. The results highlight the importance of
testing embedding models in real applications
Smooth, identifiable supermodels of discrete DAG models with latent variables
We provide a parameterization of the discrete nested Markov model, which is a
supermodel that approximates DAG models (Bayesian network models) with latent
variables. Such models are widely used in causal inference and machine
learning. We explicitly evaluate their dimension, show that they are curved
exponential families of distributions, and fit them to data. The
parameterization avoids the irregularities and unidentifiability of latent
variable models. The parameters used are all fully identifiable and
causally-interpretable quantities.Comment: 30 page
Recursive Program Optimization Through Inductive Synthesis Proof Transformation
The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality
Layered Interpretation of Street View Images
We propose a layered street view model to encode both depth and semantic
information on street view images for autonomous driving. Recently, stixels,
stix-mantics, and tiered scene labeling methods have been proposed to model
street view images. We propose a 4-layer street view model, a compact
representation over the recently proposed stix-mantics model. Our layers encode
semantic classes like ground, pedestrians, vehicles, buildings, and sky in
addition to the depths. The only input to our algorithm is a pair of stereo
images. We use a deep neural network to extract the appearance features for
semantic classes. We use a simple and an efficient inference algorithm to
jointly estimate both semantic classes and layered depth values. Our method
outperforms other competing approaches in Daimler urban scene segmentation
dataset. Our algorithm is massively parallelizable, allowing a GPU
implementation with a processing speed about 9 fps.Comment: The paper will be presented in the 2015 Robotics: Science and Systems
Conference (RSS
Bayesian model averaging over tree-based dependence structures for multivariate extremes
Describing the complex dependence structure of extreme phenomena is
particularly challenging. To tackle this issue we develop a novel statistical
algorithm that describes extremal dependence taking advantage of the inherent
hierarchical dependence structure of the max-stable nested logistic
distribution and that identifies possible clusters of extreme variables using
reversible jump Markov chain Monte Carlo techniques. Parsimonious
representations are achieved when clusters of extreme variables are found to be
completely independent. Moreover, we significantly decrease the computational
complexity of full likelihood inference by deriving a recursive formula for the
nested logistic model likelihood. The algorithm performance is verified through
extensive simulation experiments which also compare different likelihood
procedures. The new methodology is used to investigate the dependence
relationships between extreme concentration of multiple pollutants in
California and how these pollutants are related to extreme weather conditions.
Overall, we show that our approach allows for the representation of complex
extremal dependence structures and has valid applications in multivariate data
analysis, such as air pollution monitoring, where it can guide policymaking
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