pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Joint Learning of Compositional and Non-Compositional Phrase Embeddings
Authors: Kazuma Hashimoto, Yoshimasa Tsuruoka
Date: 2016-03-19 00:00:00
Proceeding: ACL 2016 8

We present a novel method for jointly learning compositional and
non-compositional phrase embeddings by adaptively weighting both types of
embeddings using a compositionality scoring function. The scoring function is
used to quantify the level of compositionality of each phrase, and the
parameters of the function are jointly optimized with the objective for
learning phrase embeddings. In experiments, we apply the adaptive joint
learning method to the task of learning embeddings of transitive verb phrases,
and show that the compositionality scores have strong correlation with human
ratings for verb-object compositionality, substantially outperforming the
previous state of the art. Moreover, our embeddings improve upon the previous
best model on a transitive verb disambiguation task. We also show that a simple
ensemble technique further improves the results for both tasks.
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Authors: Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge, Mikkel N. Schmidt, Morten Mørup
Date: 2016-01-04 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG), Functional Connectivity

Dynamic functional connectivity (FC) has in recent years become a topic of
interest in the neuroimaging community. Several models and methods exist for
both functional magnetic resonance imaging (fMRI) and electroencephalography
(EEG), and the results point towards the conclusion that FC exhibits dynamic
changes. The existing approaches modeling dynamic connectivity have primarily
been based on time-windowing the data and k-means clustering. We propose a
non-parametric generative model for dynamic FC in fMRI that does not rely on
specifying window lengths and number of dynamic states. Rooted in Bayesian
statistical modeling we use the predictive likelihood to investigate if the
model can discriminate between a motor task and rest both within and across
subjects. We further investigate what drives dynamic states using the model on
the entire data collated across subjects and task/rest. We find that the number
of states extracted are driven by subject variability and preprocessing
differences while the individual states are almost purely defined by either
task or rest. This questions how we in general interpret dynamic FC and points
to the need for more research on what drives dynamic FC.
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Authors: Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
Date: 2016-06-08 00:00:00
Tasks: Metric Learning

In decentralized networks (of sensors, connected objects, etc.), there is an
important need for efficient algorithms to optimize a global cost function, for
instance to learn a global model from the local data collected by each
computing unit. In this paper, we address the problem of decentralized
minimization of pairwise functions of the data points, where these points are
distributed over the nodes of a graph defining the communication topology of
the network. This general problem finds applications in ranking, distance
metric learning and graph inference, among others. We propose new gossip
algorithms based on dual averaging which aims at solving such problems both in
synchronous and asynchronous settings. The proposed framework is flexible
enough to deal with constrained and regularized variants of the optimization
problem. Our theoretical analysis reveals that the proposed algorithms preserve
the convergence rate of centralized dual averaging up to an additive bias term.
We present numerical simulations on Area Under the ROC Curve (AUC) maximization
and metric learning problems which illustrate the practical interest of our
approach.
Deep Reinforcement Learning with a Natural Language Action Space
Authors: Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
Date: 2015-11-14 00:00:00
Proceeding: ACL 2016 8
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

This paper introduces a novel architecture for reinforcement learning with
deep neural networks designed to handle state and action spaces characterized
by natural language, as found in text-based games. Termed a deep reinforcement
relevance network (DRRN), the architecture represents action and state spaces
with separate embedding vectors, which are combined with an interaction
function to approximate the Q-function in reinforcement learning. We evaluate
the DRRN on two popular text games, showing superior performance over other
deep Q-learning architectures. Experiments with paraphrased action descriptions
show that the model is extracting meaning rather than simply memorizing strings
of text.
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints
Authors: Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein
Date: 2016-03-29 00:00:00
Proceeding: ACL 2016 8
Tasks: Document Summarization, Sentence

We present a discriminative model for single-document summarization that
integrally combines compression and anaphoricity constraints. Our model selects
textual units to include in the summary based on a rich set of sparse features
whose weights are learned on a large corpus. We allow for the deletion of
content within a sentence when that deletion is licensed by compression rules;
in our framework, these are implemented as dependencies between subsentential
units of text. Anaphoricity constraints then improve cross-sentence coherence
by guaranteeing that, for each pronoun included in the summary, the pronoun's
antecedent is included as well or the pronoun is rewritten as a full mention.
When trained end-to-end, our final system outperforms prior work on both ROUGE
as well as on human judgments of linguistic quality.
Structured Convolution Matrices for Energy-efficient Deep learning
Authors: Rathinakumar Appuswamy, Tapan Nayak, John Arthur, Steven Esser, Paul Merolla, Jeffrey Mckinstry, Timothy Melano, Myron Flickner, Dharmendra Modha
Date: 2016-06-08 00:00:00
Tasks: Deep Learning, General Classification

We derive a relationship between network representation in energy-efficient
neuromorphic architectures and block Toplitz convolutional matrices. Inspired
by this connection, we develop deep convolutional networks using a family of
structured convolutional matrices and achieve state-of-the-art trade-off
between energy efficiency and classification accuracy for well-known image
recognition tasks. We also put forward a novel method to train binary
convolutional networks by utilising an existing connection between
noisy-rectified linear units and binary activations.
Deep Successor Reinforcement Learning
Authors: Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman
Date: 2016-06-08 00:00:00
Tasks: Deep Reinforcement Learning, FPS Games, Game of Doom, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning robust value functions given raw observations and rewards is now
possible with model-free and model-based deep reinforcement learning
algorithms. There is a third alternative, called Successor Representations
(SR), which decomposes the value function into two components -- a reward
predictor and a successor map. The successor map represents the expected future
state occupancy from any given state and the reward predictor maps states to
scalar rewards. The value function of a state can be computed as the inner
product between the successor map and the reward weights. In this paper, we
present DSR, which generalizes SR within an end-to-end deep reinforcement
learning framework. DSR has several appealing properties including: increased
sensitivity to distal reward changes due to factorization of reward and world
dynamics, and the ability to extract bottleneck states (subgoals) given
successor maps trained under a random policy. We show the efficacy of our
approach on two diverse environments given raw pixel observations -- simple
grid-world domains (MazeBase) and the Doom game engine.
A Latent-Variable Lattice Model
Authors: Rajasekaran Masatran
Date: 2015-12-23 00:00:00
Tasks: model, Quantization

Markov random field (MRF) learning is intractable, and its approximation
algorithms are computationally expensive. We target a small subset of MRF that
is used frequently in computer vision. We characterize this subset with three
concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as
an alternative. Our goal is robust learning from small datasets. Our learning
algorithm uses vector quantization and, at time complexity O(U log U) for a
dataset of U pixels, is much faster than that of general-purpose MRF.
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Authors: Shyam Upadhyay, Manaal Faruqui, Chris Dyer, Dan Roth
Date: 2016-04-01 00:00:00
Proceeding: ACL 2016 8
Tasks: Word Embeddings

Despite interest in using cross-lingual knowledge to learn word embeddings
for various tasks, a systematic comparison of the possible approaches is
lacking in the literature. We perform an extensive evaluation of four popular
approaches of inducing cross-lingual embeddings, each requiring a different
form of supervision, on four typographically different language pairs. Our
evaluation setup spans four different tasks, including intrinsic evaluation on
mono-lingual and cross-lingual similarity, and extrinsic evaluation on
downstream semantic and syntactic applications. We show that models which
require expensive cross-lingual knowledge almost always perform better, but
cheaply supervised models often prove competitive on certain tasks.
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
Authors: Petteri Teikari, Marc Santos, Charissa Poon, Kullervo Hynynen
Date: 2016-06-08 00:00:00
Tasks: 3D Architecture, Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

Recently there has been an increasing trend to use deep learning frameworks
for both 2D consumer images and for 3D medical images. However, there has been
little effort to use deep frameworks for volumetric vascular segmentation. We
wanted to address this by providing a freely available dataset of 12 annotated
two-photon vasculature microscopy stacks. We demonstrated the use of deep
learning framework consisting both 2D and 3D convolutional filters (ConvNet).
Our hybrid 2D-3D architecture produced promising segmentation result. We
derived the architectures from Lee et al. who used the ZNN framework initially
designed for electron microscope image segmentation. We hope that by sharing
our volumetric vasculature datasets, we will inspire other researchers to
experiment with vasculature dataset and improve the used network architectures.
Universal Dependencies for Learner English
Authors: Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, Jing Xian Wang, Lucia Lam, Keiko Sophie Mori, Sebastian Garza, Boris Katz
Date: 2016-05-13 00:00:00
Proceeding: ACL 2016 8
Tasks: Dependency Parsing, Language Acquisition, POS, POS Tagging, Sentence

We introduce the Treebank of Learner English (TLE), the first publicly
available syntactic treebank for English as a Second Language (ESL). The TLE
provides manually annotated POS tags and Universal Dependency (UD) trees for
5,124 sentences from the Cambridge First Certificate in English (FCE) corpus.
The UD annotations are tied to a pre-existing error annotation of the FCE,
whereby full syntactic analyses are provided for both the original and error
corrected versions of each sentence. Further on, we delineate ESL annotation
guidelines that allow for consistent syntactic treatment of ungrammatical
English. Finally, we benchmark POS tagging and dependency parsing performance
on the TLE dataset and measure the effect of grammatical errors on parsing
accuracy. We envision the treebank to support a wide range of linguistic and
computational research on second language acquisition as well as automatic
processing of ungrammatical language. The treebank is available at
universaldependencies.org. The annotation manual used in this project and a
graphical query engine are available at esltreebank.org.
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Authors: Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
Date: 2016-02-23 00:00:00
Tasks: Scheduling

In the study of human learning, there is broad evidence that our ability to
retain information improves with repeated exposure and decays with delay since
last exposure. This plays a crucial role in the design of educational software,
leading to a trade-off between teaching new material and reviewing what has
already been taught. A common way to balance this trade-off is spaced
repetition, which uses periodic review of content to improve long-term
retention. Though spaced repetition is widely used in practice, e.g., in
electronic flashcard software, there is little formal understanding of the
design of these systems. Our paper addresses this gap in three ways. First, we
mine log data from spaced repetition software to establish the functional
dependence of retention on reinforcement and delay. Second, we use this memory
model to develop a stochastic model for spaced repetition systems. We propose a
queueing network model of the Leitner system for reviewing flashcards, along
with a heuristic approximation that admits a tractable optimization problem for
review scheduling. Finally, we empirically evaluate our queueing model through
a Mechanical Turk experiment, verifying a key qualitative prediction of our
model: the existence of a sharp phase transition in learning outcomes upon
increasing the rate of new item introductions.
Stack-propagation: Improved Representation Learning for Syntax
Authors: Yuan Zhang, David Weiss
Date: 2016-03-21 00:00:00
Proceeding: ACL 2016 8
Tasks: Dependency Parsing, POS, Representation Learning

Traditional syntax models typically leverage part-of-speech (POS) information
by constructing features from hand-tuned templates. We demonstrate that a
better approach is to utilize POS tags as a regularizer of learned
representations. We propose a simple method for learning a stacked pipeline of
models which we call "stack-propagation". We apply this to dependency parsing
and tagging, where we use the hidden layer of the tagger network as a
representation of the input tokens for the parser. At test time, our parser
does not require predicted POS tags. On 19 languages from the Universal
Dependencies, our method is 1.3% (absolute) more accurate than a
state-of-the-art graph-based approach and 2.7% more accurate than the most
comparable greedy model.
Resource Constrained Structured Prediction
Authors: Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama
Date: 2016-02-28 00:00:00
Tasks: Dependency Parsing, Optical Character Recognition, Optical Character Recognition (OCR), Prediction, Structured Prediction

We study the problem of structured prediction under test-time budget
constraints. We propose a novel approach applicable to a wide range of
structured prediction problems in computer vision and natural language
processing. Our approach seeks to adaptively generate computationally costly
features during test-time in order to reduce the computational cost of
prediction while maintaining prediction performance. We show that training the
adaptive feature generation system can be reduced to a series of structured
learning problems, resulting in efficient training using existing structured
learning algorithms. This framework provides theoretical justification for
several existing heuristic approaches found in literature. We evaluate our
proposed adaptive system on two structured prediction tasks, optical character
recognition (OCR) and dependency parsing and show strong performance in
reduction of the feature costs without degrading accuracy.
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Authors: Makoto Miwa, Mohit Bansal
Date: 2016-01-05 00:00:00
Proceeding: ACL 2016 8
Tasks: Relation, Relation Classification, Relation Extraction

We present a novel end-to-end neural model to extract entities and relations
between them. Our recurrent neural network based model captures both word
sequence and dependency tree substructure information by stacking bidirectional
tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows
our model to jointly represent both entities and relations with shared
parameters in a single model. We further encourage detection of entities during
training and use of entity information in relation extraction via entity
pretraining and scheduled sampling. Our model improves over the
state-of-the-art feature-based model on end-to-end relation extraction,
achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and
ACE2004, respectively. We also show that our LSTM-RNN based model compares
favorably to the state-of-the-art CNN based model (in F1-score) on nominal
relation classification (SemEval-2010 Task 8). Finally, we present an extensive
ablation analysis of several model components.
Structure Learning in Graphical Modeling
Authors: Mathias Drton, Marloes H. Maathuis
Date: 2016-06-07 00:00:00

A graphical model is a statistical model that is associated to a graph whose
nodes correspond to variables of interest. The edges of the graph reflect
allowed conditional dependencies among the variables. Graphical models admit
computationally convenient factorization properties and have long been a
valuable tool for tractable modeling of multivariate distributions. More
recently, applications such as reconstructing gene regulatory networks from
gene expression data have driven major advances in structure learning, that is,
estimating the graph underlying a model. We review some of these advances and
discuss methods such as the graphical lasso and neighborhood selection for
undirected graphical models (or Markov random fields), and the PC algorithm and
score-based search methods for directed graphical models (or Bayesian
networks). We further review extensions that account for effects of latent
variables and heterogeneous data sources.
Active Long Term Memory Networks
Authors: Tommaso Furlanello, Jiaping Zhao, Andrew M. Saxe, Laurent Itti, Bosco S. Tjan
Date: 2016-06-07 00:00:00
Tasks: Continual Learning, Domain Adaptation

Continual Learning in artificial neural networks suffers from interference
and forgetting when different tasks are learned sequentially. This paper
introduces the Active Long Term Memory Networks (A-LTM), a model of sequential
multi-task deep learning that is able to maintain previously learned
association between sensory input and behavioral output while acquiring knew
knowledge. A-LTM exploits the non-convex nature of deep neural networks and
actively maintains knowledge of previously learned, inactive tasks using a
distillation loss. Distortions of the learned input-output map are penalized
but hidden layers are free to transverse towards new local optima that are more
favorable for the multi-task objective. We re-frame the McClelland's seminal
Hippocampal theory with respect to Catastrophic Inference (CI) behavior
exhibited by modern deep architectures trained with back-propagation and
inhomogeneous sampling of latent factors across epochs. We present empirical
results of non-trivial CI during continual learning in Deep Linear Networks
trained on the same task, in Convolutional Neural Networks when the task shifts
from predicting semantic to graphical factors and during domain adaptation from
simple to complex environments. We present results of the A-LTM model's ability
to maintain viewpoint recognition learned in the highly controlled iLab-20M
dataset with 10 object categories and 88 camera viewpoints, while adapting to
the unstructured domain of Imagenet with 1,000 object categories.
Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices
Authors: Nathan Kallus, Madeleine Udell
Date: 2015-09-17 00:00:00

We consider the problem of learning the preferences of a heterogeneous
population by observing choices from an assortment of products, ads, or other
offerings. Our observation model takes a form common in assortment planning
applications: each arriving customer is offered an assortment consisting of a
subset of all possible offerings; we observe only the assortment and the
customer's single choice.
  In this paper we propose a mixture choice model with a natural underlying
low-dimensional structure, and show how to estimate its parameters. In our
model, the preferences of each customer or segment follow a separate parametric
choice model, but the underlying structure of these parameters over all the
models has low dimension. We show that a nuclear-norm regularized maximum
likelihood estimator can learn the preferences of all customers using a number
of observations much smaller than the number of item-customer combinations.
This result shows the potential for structural assumptions to speed up learning
and improve revenues in assortment planning and customization. We provide a
specialized factored gradient descent algorithm and study the success of the
approach empirically.
Learning to Compose Neural Networks for Question Answering
Authors: Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein
Date: 2016-01-07 00:00:00
Proceeding: NAACL 2016 6
Tasks: Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We describe a question answering model that applies to both images and
structured knowledge bases. The model uses natural language strings to
automatically assemble neural networks from a collection of composable modules.
Parameters for these modules are learned jointly with network-assembly
parameters via reinforcement learning, with only (world, question, answer)
triples as supervision. Our approach, which we term a dynamic neural model
network, achieves state-of-the-art results on benchmark datasets in both visual
and structured domains.
Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions
Authors: Marius Cătălin Iordan, Armand Joulin, Diane M. Beck, Li Fei-Fei
Date: 2016-06-07 00:00:00

Inter-subject registration of cortical areas is necessary in functional
imaging (fMRI) studies for making inferences about equivalent brain function
across a population. However, many high-level visual brain areas are defined as
peaks of functional contrasts whose cortical position is highly variable. As
such, most alignment methods fail to accurately map functional regions of
interest (ROIs) across participants. To address this problem, we propose a
locally optimized registration method that directly predicts the location of a
seed ROI on a separate target cortical sheet by maximizing the functional
correlation between their time courses, while simultaneously allowing for
non-smooth local deformations in region topology. Our method outperforms the
two most commonly used alternatives (anatomical landmark-based AFNI alignment
and cortical convexity-based FreeSurfer alignment) in overlap between predicted
region and functionally-defined LOC. Furthermore, the maps obtained using our
method are more consistent across subjects than both baseline measures.
Critically, our method represents an important step forward towards predicting
brain regions without explicit localizer scans and deciphering the poorly
understood relationship between the location of functional regions, their
anatomical extent, and the consistency of computations those regions perform
across people.