pwc-archive/papers-with-abstracts huggingface dataset:
Deep Generative Models for Relational Data with Side Information
Authors: Changwei Hu, Piyush Rai, Lawrence Carin
Date: 2017-06-16 00:00:00
Proceeding: ICML 2017 8
Tasks: Data Augmentation, Link Prediction

We present a probabilistic framework for overlapping community discovery and
link prediction for relational data, given as a graph. The proposed framework
has: (1) a deep architecture which enables us to infer multiple layers of
latent features/communities for each node, providing superior link prediction
performance on more complex networks and better interpretability of the latent
features; and (2) a regression model which allows directly conditioning the
node latent features on the side information available in form of node
attributes. Our framework handles both (1) and (2) via a clean, unified model,
which enjoys full local conjugacy via data augmentation, and facilitates
efficient inference via closed form Gibbs sampling. Moreover, inference cost
scales in the number of edges which is attractive for massive but sparse
networks. Our framework is also easily extendable to model weighted networks
with count-valued edges. We compare with various state-of-the-art methods and
report results, both quantitative and qualitative, on several benchmark data
sets.
A Laplacian Framework for Option Discovery in Reinforcement Learning
Authors: Marlos C. Machado, Marc G. Bellemare, Michael Bowling
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Representation learning and option discovery are two of the biggest
challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a
well-known approach for representation learning in MDPs. In this paper we
address the option discovery problem by showing how PVFs implicitly define
options. We do it by introducing eigenpurposes, intrinsic reward functions
derived from the learned representations. The options discovered from
eigenpurposes traverse the principal directions of the state space. They are
useful for multiple tasks because they are discovered without taking the
environment's rewards into consideration. Moreover, different options act at
different time scales, making them helpful for exploration. We demonstrate
features of eigenpurposes in traditional tabular domains as well as in Atari
2600 games.
Depthwise Separable Convolutions for Neural Machine Translation
Authors: Lukasz Kaiser, Aidan N. Gomez, Francois Chollet
Date: 2017-06-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: image-classification, Machine Translation, Translation

Depthwise separable convolutions reduce the number of parameters and
computation used in convolutional operations while increasing representational
efficiency. They have been shown to be successful in image classification
models, both in obtaining better models than previously possible for a given
parameter count (the Xception architecture) and considerably reducing the
number of parameters required to perform at a given level (the MobileNets
family of architectures). Recently, convolutional sequence-to-sequence networks
have been applied to machine translation tasks with good results. In this work,
we study how depthwise separable convolutions can be applied to neural machine
translation. We introduce a new architecture inspired by Xception and ByteNet,
called SliceNet, which enables a significant reduction of the parameter count
and amount of computation needed to obtain results like ByteNet, and, with a
similar parameter count, achieves new state-of-the-art results. In addition to
showing that depthwise separable convolutions perform well for machine
translation, we investigate the architectural changes that they enable: we
observe that thanks to depthwise separability, we can increase the length of
convolution windows, removing the need for filter dilation. We also introduce a
new "super-separable" convolution operation that further reduces the number of
parameters and computational cost for obtaining state-of-the-art results.
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
Authors: Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter
Date: 2016-08-18 00:00:00
Tasks: Distributed Optimization

Discrete optimization is a central problem in artificial intelligence. The
optimization of the aggregated cost of a network of cost functions arises in a
variety of problems including (W)CSP, DCOP, as well as optimization in
stochastic variants such as the tasks of finding the most probable explanation
(MPE) in belief networks. Inference-based algorithms are powerful techniques
for solving discrete optimization problems, which can be used independently or
in combination with other techniques. However, their applicability is often
limited by their compute intensive nature and their space requirements. This
paper proposes the design and implementation of a novel inference-based
technique, which exploits modern massively parallel architectures, such as
those found in Graphical Processing Units (GPUs), to speed up the resolution of
exact and approximated inference-based algorithms for discrete optimization.
The paper studies the proposed algorithm in both centralized and distributed
optimization contexts. The paper demonstrates that the use of GPUs provides
significant advantages in terms of runtime and scalability, achieving up to two
orders of magnitude in speedups and showing a considerable reduction in
execution time (up to 345 times faster) with respect to a sequential version.
The Off-Switch Game
Authors: Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, Stuart Russell
Date: 2016-11-24 00:00:00

It is clear that one of the primary tools we can use to mitigate the
potential risk from a misbehaving AI system is the ability to turn the system
off. As the capabilities of AI systems improve, it is important to ensure that
such systems do not adopt subgoals that prevent a human from switching them
off. This is a challenge because many formulations of rational agents create
strong incentives for self-preservation. This is not caused by a built-in
instinct, but because a rational agent will maximize expected utility and
cannot achieve whatever objective it has been given if it is dead. Our goal is
to study the incentives an agent has to allow itself to be switched off. We
analyze a simple game between a human H and a robot R, where H can press R's
off switch but R can disable the off switch. A traditional agent takes its
reward function for granted: we show that such agents have an incentive to
disable the off switch, except in the special case where H is perfectly
rational. Our key insight is that for R to want to preserve its off switch, it
needs to be uncertain about the utility associated with the outcome, and to
treat H's actions as important observations about that utility. (R also has no
incentive to switch itself off in this setting.) We conclude that giving
machines an appropriate level of uncertainty about their objectives leads to
safer designs, and we argue that this setting is a useful generalization of the
classical AI paradigm of rational agents.
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Authors: Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra
Date: 2017-06-16 00:00:00

Much of human dialogue occurs in semi-cooperative settings, where agents with
different goals attempt to agree on common decisions. Negotiations require
complex communication and reasoning skills, but success is easy to measure,
making this an interesting task for AI. We gather a large dataset of
human-human negotiations on a multi-issue bargaining task, where agents who
cannot observe each other's reward functions must reach an agreement (or a
deal) via natural language dialogue. For the first time, we show it is possible
to train end-to-end models for negotiation, which must learn both linguistic
and reasoning skills with no annotated dialogue states. We also introduce
dialogue rollouts, in which the model plans ahead by simulating possible
complete continuations of the conversation, and find that this technique
dramatically improves performance. Our code and dataset are publicly available
(https://github.com/facebookresearch/end-to-end-negotiator).
Deriving Compact Laws Based on Algebraic Formulation of a Data Set
Authors: Wenqing Xu, Mark Stalzer
Date: 2017-06-16 00:00:00

In various subjects, there exist compact and consistent relationships between
input and output parameters. Discovering the relationships, or namely compact
laws, in a data set is of great interest in many fields, such as physics,
chemistry, and finance. While data discovery has made great progress in
practice thanks to the success of machine learning in recent years, the
development of analytical approaches in finding the theory behind the data is
relatively slow. In this paper, we develop an innovative approach in
discovering compact laws from a data set. By proposing a novel algebraic
equation formulation, we convert the problem of deriving meaning from data into
formulating a linear algebra model and searching for relationships that fit the
data. Rigorous proof is presented in validating the approach. The algebraic
formulation allows the search of equation candidates in an explicit
mathematical manner. Searching algorithms are also proposed for finding the
governing equations with improved efficiency. For a certain type of compact
theory, our approach assures convergence and the discovery is computationally
efficient and mathematically precise.
A Mixture Model for Learning Multi-Sense Word Embeddings
Authors: Dai Quoc Nguyen, Dat Quoc Nguyen, Ashutosh Modi, Stefan Thater, Manfred Pinkal
Date: 2017-06-15 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Embeddings

Word embeddings are now a standard technique for inducing meaning
representations for words. For getting good representations, it is important to
take into account different senses of a word. In this paper, we propose a
mixture model for learning multi-sense word embeddings. Our model generalizes
the previous works in that it allows to induce different weights of different
senses of a word. The experimental results show that our model outperforms
previous models on standard evaluation tasks.
On the enumeration of sentences by compactness
Authors: Mark A. Stalzer
Date: 2017-06-15 00:00:00

Presented is a Julia meta-program that discovers compact theories from data
if they exist. It writes candidate theories in Julia and then validates:
tossing the bad theories and keeping the good theories. Compactness is measured
by a metric: such as the number of space-time derivatives. The underlying
algorithm is applicable to a wide variety of combinatorics problems and
compactness serves to cut down the search space.
Low rank approximation and decomposition of large matrices using error correcting codes
Authors: Shashanka Ubaru, Arya Mazumdar, Yousef Saad
Date: 2015-12-30 00:00:00
Tasks: regression

Low rank approximation is an important tool used in many applications of
signal processing and machine learning. Recently, randomized sketching
algorithms were proposed to effectively construct low rank approximations and
obtain approximate singular value decompositions of large matrices. Similar
ideas were used to solve least squares regression problems. In this paper, we
show how matrices from error correcting codes can be used to find such low rank
approximations and matrix decompositions, and extend the framework to linear
least squares regression problems. The benefits of using these code matrices
are the following: (i) They are easy to generate and they reduce randomness
significantly. (ii) Code matrices with mild properties satisfy the subspace
embedding property, and have a better chance of preserving the geometry of an
entire subspace of vectors. (iii) For parallel and distributed applications,
code matrices have significant advantages over structured random matrices and
Gaussian random matrices. (iv) Unlike Fourier or Hadamard transform matrices,
which require sampling $O(k\log k)$ columns for a rank-$k$ approximation, the
log factor is not necessary for certain types of code matrices. That is,
$(1+\epsilon)$ optimal Frobenius norm error can be achieved for a rank-$k$
approximation with $O(k/\epsilon)$ samples. (v) Fast multiplication is possible
with structured code matrices, so fast approximations can be achieved for
general dense input matrices. (vi) For least squares regression problem
$\min\|Ax-b\|_2$ where $A\in \mathbb{R}^{n\times d}$, the $(1+\epsilon)$
relative error approximation can be achieved with $O(d/\epsilon)$ samples, with
high probability, when certain code matrices are used.
Symplectomorphic registration with phase space regularization by entropy spectrum pathways
Authors: Vitaly L. Galinsky, Lawrence R. Frank
Date: 2017-06-15 00:00:00
Tasks: CPU

The ability to register image data to a common coordinate system is a
critical feature of virtually all imaging studies that require multiple subject
analysis, combining single subject data from multiple modalities, or both.
However, in spite of the abundance of literature on the subject and the
existence of several variants of registration algorithms, their practical
utility remains problematic, as commonly acknowledged even by developers of
these methods because the complexity of the problem has resisted a general,
flexible, and robust theoretical and computational framework.
  To address this issue, we present a new registration method that is similar
in spirit to the current state-of-the-art technique of diffeomorphic mapping,
but is more general and flexible. The method utilizes a Hamiltonian formalism
and constructs registration as a sequence of symplectomorphic maps in
conjunction with a novel phase space regularization based on the powerful
entropy spectrum pathways (ESP) framework.
  The method is demonstrated on the three different magnetic resonance imaging
(MRI) modalities routinely used for human neuroimaging applications by mapping
between high resolution anatomical (HRA) volumes, medium resolution diffusion
weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI)
and HRA volumes. The typical processing time for high quality mapping ranges
from less than a minute to several minutes on a modern multi core CPU for
typical high resolution anatomical (~256x256x256 voxels) MRI volumes.
An Overview of Multi-Task Learning in Deep Neural Networks
Authors: Sebastian Ruder
Date: 2017-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, Multi-Task Learning, speech-recognition, Speech Recognition

Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for MTL in Deep Learning, gives an overview of the
literature, and discusses recent advances. In particular, it seeks to help ML
practitioners apply MTL by shedding light on how MTL works and providing
guidelines for choosing appropriate auxiliary tasks.
The "something something" video database for learning and evaluating visual common sense
Authors: Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzyńska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, Roland Memisevic
Date: 2017-06-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Common Sense Reasoning, General Classification, Video Prediction

Neural networks trained on datasets such as ImageNet have led to major
advances in visual object classification. One obstacle that prevents networks
from reasoning more deeply about complex scenes and situations, and from
integrating visual knowledge with natural language, like humans do, is their
lack of common sense knowledge about the physical world. Videos, unlike still
images, contain a wealth of detailed information about the physical world.
However, most labelled video datasets represent high-level concepts rather than
detailed physical aspects about actions and scenes. In this work, we describe
our ongoing collection of the "something-something" database of video
prediction tasks whose solutions require a common sense understanding of the
depicted situation. The database currently contains more than 100,000 videos
across 174 classes, which are defined as caption-templates. We also describe
the challenges in crowd-sourcing this data at scale.
Learning Disjunctions of Predicates
Authors: Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev, Eran Yahav
Date: 2017-06-15 00:00:00
Tasks: Program Synthesis

Let $F$ be a set of boolean functions. We present an algorithm for learning
$F_\vee := \{\vee_{f\in S} f \mid S \subseteq F\}$ from membership queries. Our
algorithm asks at most $|F| \cdot OPT(F_\vee)$ membership queries where
$OPT(F_\vee)$ is the minimum worst case number of membership queries for
learning $F_\vee$. When $F$ is a set of halfspaces over a constant dimension
space or a set of variable inequalities, our algorithm runs in polynomial time.
  The problem we address has practical importance in the field of program
synthesis, where the goal is to synthesize a program that meets some
requirements. Program synthesis has become popular especially in settings
aiming to help end users. In such settings, the requirements are not provided
upfront and the synthesizer can only learn them by posing membership queries to
the end user. Our work enables such synthesizers to learn the exact
requirements while bounding the number of membership queries.
Generalization for Adaptively-chosen Estimators via Stable Median
Authors: Vitaly Feldman, Thomas Steinke
Date: 2017-06-15 00:00:00

Datasets are often reused to perform multiple statistical analyses in an
adaptive way, in which each analysis may depend on the outcomes of previous
analyses on the same dataset. Standard statistical guarantees do not account
for these dependencies and little is known about how to provably avoid
overfitting and false discovery in the adaptive setting. We consider a natural
formalization of this problem in which the goal is to design an algorithm that,
given a limited number of i.i.d.~samples from an unknown distribution, can
answer adaptively-chosen queries about that distribution.
  We present an algorithm that estimates the expectations of $k$ arbitrary
adaptively-chosen real-valued estimators using a number of samples that scales
as $\sqrt{k}$. The answers given by our algorithm are essentially as accurate
as if fresh samples were used to evaluate each estimator. In contrast, prior
work yields error guarantees that scale with the worst-case sensitivity of each
estimator. We also give a version of our algorithm that can be used to verify
answers to such queries where the sample complexity depends logarithmically on
the number of queries $k$ (as in the reusable holdout technique).
  Our algorithm is based on a simple approximate median algorithm that
satisfies the strong stability guarantees of differential privacy. Our
techniques provide a new approach for analyzing the generalization guarantees
of differentially private algorithms.
Face Clustering: Representation and Pairwise Constraints
Authors: Yichun Shi, Charles Otto, Anil K. Jain
Date: 2017-06-15 00:00:00
Tasks: Clustering, Face Clustering, image-classification, Image Classification, Retrieval

Clustering face images according to their identity has two important
applications: (i) grouping a collection of face images when no external labels
are associated with images, and (ii) indexing for efficient large scale face
retrieval. The clustering problem is composed of two key parts: face
representation and choice of similarity for grouping faces. We first propose a
representation based on ResNet, which has been shown to perform very well in
image classification problems. Given this representation, we design a
clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly
estimates the adjacency matrix only based on the similarity between face
images. This allows a dynamic selection of number of clusters and retains
pairwise similarity between faces. ConPaC formulates the clustering problem as
a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to
find an approximate solution for maximizing the posterior probability of the
adjacency matrix. Experimental results on two benchmark face datasets (LFW and
IJB-B) show that ConPaC outperforms well known clustering algorithms such as
k-means, spectral clustering and approximate rank-order. Additionally, our
algorithm can naturally incorporate pairwise constraints to obtain a
semi-supervised version that leads to improved clustering performance. We also
propose an k-NN variant of ConPaC, which has a linear time complexity given a
k-NN graph, suitable for large datasets.
Learning and Evaluating Musical Features with Deep Autoencoders
Authors: Mason Bretan, Sageev Oore, Doug Eck, Larry Heck
Date: 2017-06-14 00:00:00
Tasks: Denoising, General Classification

In this work we describe and evaluate methods to learn musical embeddings.
Each embedding is a vector that represents four contiguous beats of music and
is derived from a symbolic representation. We consider autoencoding-based
methods including denoising autoencoders, and context reconstruction, and
evaluate the resulting embeddings on a forward prediction and a classification
task.
Conjunctions of Among Constraints
Authors: Victor Dalmau
Date: 2017-06-15 00:00:00

Many existing global constraints can be encoded as a conjunction of among
constraints. An among constraint holds if the number of the variables in its
scope whose value belongs to a prespecified set, which we call its range, is
within some given bounds. It is known that domain filtering algorithms can
benefit from reasoning about the interaction of among constraints so that
values can be filtered out taking into consideration several among constraints
simultaneously. The present pa- per embarks into a systematic investigation on
the circumstances under which it is possible to obtain efficient and complete
domain filtering algorithms for conjunctions of among constraints. We start by
observing that restrictions on both the scope and the range of the among
constraints are necessary to obtain meaningful results. Then, we derive a
domain flow-based filtering algorithm and present several applications. In
particular, it is shown that the algorithm unifies and generalizes several
previous existing results.
Global optimization of Lipschitz functions
Authors: Cédric Malherbe, Nicolas Vayatis
Date: 2017-03-07 00:00:00
Proceeding: ICML 2017 8
Tasks: global-optimization, Hyperparameter Optimization

The goal of the paper is to design sequential strategies which lead to
efficient optimization of an unknown function under the only assumption that it
has a finite Lipschitz constant. We first identify sufficient conditions for
the consistency of generic sequential algorithms and formulate the expected
minimax rate for their performance. We introduce and analyze a first algorithm
called LIPO which assumes the Lipschitz constant to be known. Consistency,
minimax rates for LIPO are proved, as well as fast rates under an additional
H\"older like condition. An adaptive version of LIPO is also introduced for the
more realistic setup where the Lipschitz constant is unknown and has to be
estimated along with the optimization. Similar theoretical guarantees are shown
to hold for the adaptive LIPO algorithm and a numerical assessment is provided
at the end of the paper to illustrate the potential of this strategy with
respect to state-of-the-art methods over typical benchmark problems for global
optimization.