pwc-archive/papers-with-abstracts huggingface dataset:
The Promise and Peril of Human Evaluation for Model Interpretability
Authors: Bernease Herman
Date: 2017-11-20 00:00:00
Tasks: Descriptive, Interpretable Machine Learning, Position

Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this position paper, we propose a distinction between descriptive and persuasive explanations. We discuss reasoning suggesting that functional interpretability may be correlated with cognitive function and user preferences. If this is indeed the case, evaluation and optimization using functional metrics could perpetuate implicit cognitive bias in explanations that threaten transparency. Finally, we propose two potential research directions to disambiguate cognitive function and explanation models, retaining control over the tradeoff between accuracy and interpretability.
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
Authors: Vivek Bheda, Dianna Radpour
Date: 2017-10-18 00:00:00
Tasks: Gesture Recognition

In the realm of multimodal communication, sign language is, and continues to
be, one of the most understudied areas. In line with recent advances in the
field of deep learning, there are far reaching implications and applications
that neural networks can have for sign language interpretation. In this paper,
we present a method for using deep convolutional networks to classify images of
both the the letters and digits in American Sign Language.
Non-Contextual Modeling of Sarcasm using a Neural Network Benchmark
Authors: N. Dianna Radpour, Vinay Ashokkumar
Date: 2017-11-20 00:00:00
Tasks: Benchmarking, Sentiment Analysis

One of the most crucial components of natural human-robot interaction is
artificial intuition and its influence on dialog systems. The intuitive
capability that humans have is undeniably extraordinary, and so remains one of
the greatest challenges for natural communicative dialogue between humans and
robots. In this paper, we introduce a novel probabilistic modeling framework of
identifying, classifying and learning features of sarcastic text via training a
neural network with human-informed sarcastic benchmarks. This is necessary for
establishing a comprehensive sentiment analysis schema that is sensitive to the
nuances of sarcasm-ridden text by being trained on linguistic cues. We show
that our model provides a good fit for this type of real-world informed data,
with potential to achieve as accurate, if not more, than alternatives. Though
the implementation and benchmarking is an extensive task, it can be extended
via the same method that we present to capture different forms of nuances in
communication and making for much more natural and engaging dialogue systems.
Teaching a Machine to Read Maps with Deep Reinforcement Learning
Authors: Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer
Date: 2017-11-20 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability to use a 2D map to navigate a complex 3D environment is quite
remarkable, and even difficult for many humans. Localization and navigation is
also an important problem in domains such as robotics, and has recently become
a focus of the deep reinforcement learning community. In this paper we teach a
reinforcement learning agent to read a map in order to find the shortest way
out of a random maze it has never seen before. Our system combines several
state-of-the-art methods such as A3C and incorporates novel elements such as a
recurrent localization cell. Our agent learns to localize itself based on 3D
first person images and an approximate orientation angle. The agent generalizes
well to bigger mazes, showing that it learned useful localization and
navigation capabilities.
Implementing the Deep Q-Network
Authors: Melrose Roderick, James Macglashan, Stefanie Tellex
Date: 2017-11-20 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Reinforcement Learning

The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and
building point for much deep reinforcement learning research. However,
replicating results for complex systems is often challenging since original
scientific publications are not always able to describe in detail every
important parameter setting and software engineering solution. In this paper,
we present results from our work reproducing the results of the DQN paper. We
highlight key areas in the implementation that were not covered in great detail
in the original paper to make it easier for researchers to replicate these
results, including termination conditions and gradient descent algorithms.
Finally, we discuss methods for improving the computational performance and
provide our own implementation that is designed to work with a range of
domains, and not just the original Arcade Learning Environment [Bellemare et
al., 2013].
Detecting Adversarial Samples Using Density Ratio Estimates
Authors: Lovedeep Gondara
Date: 2017-05-05 00:00:00
Tasks: Density Ratio Estimation, Self-Driving Cars

Machine learning models, especially based on deep architectures are used in
everyday applications ranging from self driving cars to medical diagnostics. It
has been shown that such models are dangerously susceptible to adversarial
samples, indistinguishable from real samples to human eye, adversarial samples
lead to incorrect classifications with high confidence. Impact of adversarial
samples is far-reaching and their efficient detection remains an open problem.
We propose to use direct density ratio estimation as an efficient model
agnostic measure to detect adversarial samples. Our proposed method works
equally well with single and multi-channel samples, and with different
adversarial sample generation methods. We also propose a method to use density
ratio estimates for generating adversarial samples with an added constraint of
preserving density ratio.
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Authors: Asish Ghoshal, Jean Honorio
Date: 2017-06-18 00:00:00

We consider the problem of learning sparse polymatrix games from observations
of strategic interactions. We show that a polynomial time method based on
$\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash
equilibria are the $\epsilon$-Nash equilibria of the game from which the data
was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of
strategy profiles --- where $m$ is the maximum number of pure strategies of a
player, $p$ is the number of players, and $d$ is the maximum degree of the game
graph. Under slightly more stringent separability conditions on the payoff
matrices of the true game, we show that our method learns a game with the exact
same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$
samples are necessary for any method to consistently recover a game, with the
same Nash-equilibria as the true game, from observations of strategic
interactions. We verify our theoretical results through simulation experiments.
The Partially Observable Hidden Markov Model and its Application to Keystroke Dynamics
Authors: John V. Monaco, Charles C. Tappert
Date: 2016-07-13 00:00:00

The partially observable hidden Markov model is an extension of the hidden
Markov Model in which the hidden state is conditioned on an independent Markov
chain. This structure is motivated by the presence of discrete metadata, such
as an event type, that may partially reveal the hidden state but itself
emanates from a separate process. Such a scenario is encountered in keystroke
dynamics whereby a user's typing behavior is dependent on the text that is
typed. Under the assumption that the user can be in either an active or passive
state of typing, the keyboard key names are event types that partially reveal
the hidden state due to the presence of relatively longer time intervals
between words and sentences than between letters of a word. Using five public
datasets, the proposed model is shown to consistently outperform other anomaly
detectors, including the standard HMM, in biometric identification and
verification tasks and is generally preferred over the HMM in a Monte Carlo
goodness of fit test.
Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores
Authors: Elham Taghizadeh
Date: 2017-11-20 00:00:00
Tasks: Demand Forecasting, Management

One key requirement for effective supply chain management is the quality of
its inventory management. Various inventory management methods are typically
employed for different types of products based on their demand patterns,
product attributes, and supply network. In this paper, our goal is to develop
robust demand prediction methods for weather sensitive products at retail
stores. We employ historical datasets from Walmart, whose customers and markets
are often exposed to extreme weather events which can have a huge impact on
sales regarding the affected stores and products. We want to accurately predict
the sales of 111 potentially weather-sensitive products around the time of
major weather events at 45 of Walmart retails locations in the U.S.
Intuitively, we may expect an uptick in the sales of umbrellas before a big
thunderstorm, but it is difficult for replenishment managers to predict the
level of inventory needed to avoid being out-of-stock or overstock during and
after that storm. While they rely on a variety of vendor tools to predict sales
around extreme weather events, they mostly employ a time-consuming process that
lacks a systematic measure of effectiveness. We employ all the methods critical
to any analytics project and start with data exploration. Critical features are
extracted from the raw historical dataset for demand forecasting accuracy and
robustness. In particular, we employ Artificial Neural Network for forecasting
demand for each product sold around the time of major weather events. Finally,
we evaluate our model to evaluate their accuracy and robustness.
Recover Missing Sensor Data with Iterative Imputing Network
Authors: Jingguang Zhou, Zili Huang
Date: 2017-11-20 00:00:00
Tasks: Missing Values

Sensor data has been playing an important role in machine learning tasks,
complementary to the human-annotated data that is usually rather costly.
However, due to systematic or accidental mis-operations, sensor data comes very
often with a variety of missing values, resulting in considerable difficulties
in the follow-up analysis and visualization. Previous work imputes the missing
values by interpolating in the observational feature space, without consulting
any latent (hidden) dynamics. In contrast, our model captures the latent
complex temporal dynamics by summarizing each observation's context with a
novel Iterative Imputing Network, thus significantly outperforms previous work
on the benchmark Beijing air quality and meteorological dataset. Our model also
yields consistent superiority over other methods in cases of different missing
rates.
Pixel-wise object tracking
Authors: Yilin Song, Chenge Li, Yao Wang
Date: 2017-11-20 00:00:00
Tasks: Object, Object Tracking, Segmentation, Semantic Segmentation, Visual Object Tracking

In this paper, we propose a novel pixel-wise visual object tracking framework
that can track any anonymous object in a noisy background. The framework
consists of two submodels, a global attention model and a local segmentation
model. The global model generates a region of interests (ROI) that the object
may lie in the new frame based on the past object segmentation maps, while the
local model segments the new image in the ROI. Each model uses a LSTM structure
to model the temporal dynamics of the motion and appearance, respectively. To
circumvent the dependency of the training data between the two models, we use
an iterative update strategy. Once the models are trained, there is no need to
refine them to track specific objects, making our method efficient compared to
online learning approaches. We demonstrate our real time pixel-wise object
tracking framework on a challenging VOT dataset
Classification with Costly Features using Deep Reinforcement Learning
Authors: Jaromír Janisch, Tomáš Pevný, Viliam Lisý
Date: 2017-11-20 00:00:00
Tasks: Classification, Classification with Costly Features, Decision Making, Deep Reinforcement Learning, General Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize a trade-off between the expected
classification error and the feature cost. We revisit a former approach that
has framed the problem as a sequential decision-making problem and solved it by
Q-learning with a linear approximation, where individual actions are either
requests for feature values or terminate the episode by providing a
classification decision. On a set of eight problems, we demonstrate that by
replacing the linear approximation with neural networks the approach becomes
comparable to the state-of-the-art algorithms developed specifically for this
problem. The approach is flexible, as it can be improved with any new
reinforcement learning enhancement, it allows inclusion of pre-trained
high-performance classifier, and unlike prior art, its performance is robust
across all evaluated datasets.
Community detection with spiking neural networks for neuromorphic hardware
Authors: Kathleen E. Hamilton, Neena Imam, Travis S. Humble
Date: 2017-11-20 00:00:00
Tasks: Community Detection

We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Authors: Ziming Zhang, Matthew Brand
Date: 2017-11-20 00:00:00
Proceeding: NeurIPS 2017 12

By lifting the ReLU function into a higher dimensional space, we develop a
smooth multi-convex formulation for training feed-forward deep neural networks
(DNNs). This allows us to develop a block coordinate descent (BCD) training
algorithm consisting of a sequence of numerically well-behaved convex
optimizations. Using ideas from proximal point methods in convex analysis, we
prove that this BCD algorithm will converge globally to a stationary point with
R-linear convergence rate of order one. In experiments with the MNIST database,
DNNs trained with this BCD algorithm consistently yielded better test-set error
rates than identical DNN architectures trained via all the stochastic gradient
descent (SGD) variants in the Caffe toolbox.
Bayesian Active Edge Evaluation on Expensive Graphs
Authors: Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer
Date: 2017-11-20 00:00:00
Tasks: Active Learning, Motion Planning, valid

Robots operate in environments with varying implicit structure. For instance,
a helicopter flying over terrain encounters a very different arrangement of
obstacles than a robotic arm manipulating objects on a cluttered table top.
State-of-the-art motion planning systems do not exploit this structure, thereby
expending valuable planning effort searching for implausible solutions. We are
interested in planning algorithms that actively infer the underlying structure
of the valid configuration space during planning in order to find solutions
with minimal effort. Consider the problem of evaluating edges on a graph to
quickly discover collision-free paths. Evaluating edges is expensive, both for
robots with complex geometries like robot arms, and for robots with limited
onboard computation like UAVs. Until now, this challenge has been addressed via
laziness i.e. deferring edge evaluation until absolutely necessary, with the
hope that edges turn out to be valid. However, all edges are not alike in value
- some have a lot of potentially good paths flowing through them, and some
others encode the likelihood of neighbouring edges being valid. This leads to
our key insight - instead of passive laziness, we can actively choose edges
that reduce the uncertainty about the validity of paths. We show that this is
equivalent to the Bayesian active learning paradigm of decision region
determination (DRD). However, the DRD problem is not only combinatorially hard,
but also requires explicit enumeration of all possible worlds. We propose a
novel framework that combines two DRD algorithms, DIRECT and BISECT, to
overcome both issues. We show that our approach outperforms several
state-of-the-art algorithms on a spectrum of planning problems for mobile
robots, manipulators and autonomous helicopters.
An Unsupervised Approach for Mapping between Vector Spaces
Authors: Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee, Arjit Srivastava, Madan Gopal Jhawar, Manish Shrivastava
Date: 2017-11-15 00:00:00
Tasks: Word Embeddings, Word Similarity

We present a language independent, unsupervised approach for transforming
word embeddings from source language to target language using a transformation
matrix. Our model handles the problem of data scarcity which is faced by many
languages in the world and yields improved word embeddings for words in the
target language by relying on transformed embeddings of words of the source
language. We initially evaluate our approach via word similarity tasks on a
similar language pair - Hindi as source and Urdu as the target language, while
we also evaluate our method on French and German as target languages and
English as source language. Our approach improves the current state of the art
results - by 13% for French and 19% for German. For Urdu, we saw an increment
of 16% over our initial baseline score. We further explore the prospects of our
approach by applying it on multiple models of the same language and
transferring words between the two models, thus solving the problem of missing
words in a model. We evaluate this on word similarity and word analogy tasks.
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
Authors: Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff
Date: 2017-04-13 00:00:00

Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n
\times n}$ is a fundamental task in countless applications. In matrix
multiplication time, it is possible to perform a full SVD and directly compute
the singular values $\sigma_1,...,\sigma_n$. However, little is known about
algorithms that break this runtime barrier.
  Using tools from stochastic trace estimation, polynomial approximation, and
fast system solvers, we show how to efficiently isolate different ranges of
$A$'s spectrum and approximate the number of singular values in these ranges.
We thus effectively compute a histogram of the spectrum, which can stand in for
the true singular values in many applications.
  We use this primitive to give the first algorithms for approximating a wide
class of symmetric matrix norms in faster than matrix multiplication time. For
example, we give a $(1 + \epsilon)$ approximation algorithm for the
Schatten-$1$ norm (the nuclear norm) running in just $\tilde O((nnz(A)n^{1/3} +
n^2)\epsilon^{-3})$ time for $A$ with uniform row sparsity or $\tilde
O(n^{2.18} \epsilon^{-3})$ time for dense matrices. The runtime scales smoothly
for general Schatten-$p$ norms, notably becoming $\tilde O (p \cdot nnz(A)
\epsilon^{-3})$ for any $p \ge 2$.
  At the same time, we show that the complexity of spectrum approximation is
inherently tied to fast matrix multiplication in the small $\epsilon$ regime.
We prove that achieving milder $\epsilon$ dependencies in our algorithms would
imply faster than matrix multiplication time triangle detection for general
graphs. This further implies that highly accurate algorithms running in
subcubic time yield subcubic time matrix multiplication. As an application of
our bounds, we show that precisely computing all effective resistances in a
graph in less than matrix multiplication time is likely difficult, barring a
major algorithmic breakthrough.
Optimised Maintenance of Datalog Materialisations
Authors: Pan Hu, Boris Motik, Ian Horrocks
Date: 2017-11-10 00:00:00

To efficiently answer queries, datalog systems often materialise all
consequences of a datalog program, so the materialisation must be updated
whenever the input facts change. Several solutions to the materialisation
update problem have been proposed. The Delete/Rederive (DRed) and the
Backward/Forward (B/F) algorithms solve this problem for general datalog, but
both contain steps that evaluate rules 'backwards' by matching their heads to a
fact and evaluating the partially instantiated rule bodies as queries. We show
that this can be a considerable source of overhead even on very small updates.
In contrast, the Counting algorithm does not evaluate the rules 'backwards',
but it can handle only nonrecursive rules. We present two hybrid approaches
that combine DRed and B/F with Counting so as to reduce or even eliminate
'backward' rule evaluation while still handling arbitrary datalog programs. We
show empirically that our hybrid algorithms are usually significantly faster
than existing approaches, sometimes by orders of magnitude.
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit
Authors: Bo Zhao, Xinwei Sun, Yuan YAO, Yizhou Wang
Date: 2017-11-20 00:00:00
Tasks: Clustering, Generalized Zero-Shot Learning, Transfer Learning, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize objects from novel unseen classes
without any training data. Recently, structure-transfer based methods are
proposed to implement ZSL by transferring structural knowledge from the
semantic embedding space to image feature space to classify testing images.
However, we observe that such a knowledge transfer framework may suffer from
the problem of the geometric inconsistency between the data in the training and
testing spaces. We call this problem as the space shift problem. In this paper,
we propose a novel graph based method to alleviate this space shift problem.
Specifically, a Shared Reconstruction Graph (SRG) is pursued to capture the
common structure of data in the two spaces. With the learned SRG, each unseen
class prototype (cluster center) in the image feature space can be synthesized
by the linear combination of other class prototypes, so that testing instances
can be classified based on the distance to these synthesized prototypes. The
SRG bridges the image feature space and semantic embedding space. By applying
spectral clustering on the learned SRG, many meaningful clusters can be
discovered, which interprets ZSL performance on the datasets. Our method can be
easily extended to the generalized zero-shot learning setting. Experiments on
three popular datasets show that our method outperforms other methods on all
datasets. Even with a small number of training samples, our method can achieve
the state-of-the-art performance.