pwc-archive/papers-with-abstracts huggingface dataset:
NeVAE: A Deep Generative Model for Molecular Graphs
Authors: Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez
Date: 2018-02-14 00:00:00
Tasks: Bayesian Optimization, Decoder, model, Reinforcement Learning

Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics-their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we first propose a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates. Then, we develop a gradient-based algorithm to optimize the decoder of our model so that it learns to generate molecules that maximize the value of certain property of interest and, given a molecule of interest, it is able to optimize the spatial configuration of its atoms for greater stability. Experiments reveal that our variational autoencoder can discover plausible, diverse and novel molecules more effectively than several state of the art models. Moreover, for several properties of interest, our optimized decoder is able to identify molecules with property values 121% higher than those identified by several state of the art methods based on Bayesian optimization and reinforcement learning
Communication Algorithms via Deep Learning
Authors: Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2018-05-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning, Ingenuity

Coding theory is a central discipline underpinning wireline and wireless
modems that are the workhorses of the information age. Progress in coding
theory is largely driven by individual human ingenuity with sporadic
breakthroughs over the past century. In this paper we study whether it is
possible to automate the discovery of decoding algorithms via deep learning. We
study a family of sequential codes parameterized by recurrent neural network
(RNN) architectures. We show that creatively designed and trained RNN
architectures can decode well known sequential codes such as the convolutional
and turbo codes with close to optimal performance on the additive white
Gaussian noise (AWGN) channel, which itself is achieved by breakthrough
algorithms of our times (Viterbi and BCJR decoders, representing dynamic
programing and forward-backward algorithms). We show strong generalizations,
i.e., we train at a specific signal to noise ratio and block length but test at
a wide range of these quantities, as well as robustness and adaptivity to
deviations from the AWGN setting.
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Authors: Beilun Wang, Arshdeep Sekhon, Yanjun Qi
Date: 2017-10-30 00:00:00

We focus on the problem of estimating the change in the dependency structures
of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for
sparse change estimation in GGMs involve expensive and difficult non-smooth
optimization. We propose a novel method, DIFFEE for estimating DIFFerential
networks via an Elementary Estimator under a high-dimensional situation. DIFFEE
is solved through a faster and closed form solution that enables it to work in
large-scale settings. We conduct a rigorous statistical analysis showing that
surprisingly DIFFEE achieves the same asymptotic convergence rates as the
state-of-the-art estimators that are much more difficult to compute. Our
experimental results on multiple synthetic datasets and one real-world data
about brain connectivity show strong performance improvements over baselines,
as well as significant computational benefits.
End-to-End Speech-Driven Facial Animation with Temporal GANs
Authors: Konstantinos Vougioukas, Stavros Petridis, Maja Pantic
Date: 2018-05-23 00:00:00
Tasks: Lip Reading

Speech-driven facial animation is the process which uses speech signals to
automatically synthesize a talking character. The majority of work in this
domain creates a mapping from audio features to visual features. This often
requires post-processing using computer graphics techniques to produce
realistic albeit subject dependent results. We present a system for generating
videos of a talking head, using a still image of a person and an audio clip
containing speech, that doesn't rely on any handcrafted intermediate features.
To the best of our knowledge, this is the first method capable of generating
subject independent realistic videos directly from raw audio. Our method can
generate videos which have (a) lip movements that are in sync with the audio
and (b) natural facial expressions such as blinks and eyebrow movements. We
achieve this by using a temporal GAN with 2 discriminators, which are capable
of capturing different aspects of the video. The effect of each component in
our system is quantified through an ablation study. The generated videos are
evaluated based on their sharpness, reconstruction quality, and lip-reading
accuracy. Finally, a user study is conducted, confirming that temporal GANs
lead to more natural sequences than a static GAN-based approach.
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
Authors: Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su
Date: 2018-01-18 00:00:00
Proceeding: ACL 2018 7
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Task-Completion Dialogue Policy Learning

Training a task-completion dialogue agent via reinforcement learning (RL) is
costly because it requires many interactions with real users. One common
alternative is to use a user simulator. However, a user simulator usually lacks
the language complexity of human interlocutors and the biases in its design may
tend to degrade the agent. To address these issues, we present Deep Dyna-Q,
which to our knowledge is the first deep RL framework that integrates planning
for task-completion dialogue policy learning. We incorporate into the dialogue
agent a model of the environment, referred to as the world model, to mimic real
user response and generate simulated experience. During dialogue policy
learning, the world model is constantly updated with real user experience to
approach real user behavior, and in turn, the dialogue agent is optimized using
both real experience and simulated experience. The effectiveness of our
approach is demonstrated on a movie-ticket booking task in both simulated and
human-in-the-loop settings.
Input and Weight Space Smoothing for Semi-supervised Learning
Authors: Safa Cicek, Stefano Soatto
Date: 2018-05-23 00:00:00
Tasks: Data Augmentation

We propose regularizing the empirical loss for semi-supervised learning by
acting on both the input (data) space, and the weight (parameter) space. We
show that the two are not equivalent, and in fact are complementary, one
affecting the minimality of the resulting representation, the other
insensitivity to nuisance variability. We propose a method to perform such
smoothing, which combines known input-space smoothing with a novel weight-space
smoothing, based on a min-max (adversarial) optimization. The resulting
Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent
with a small learning rate for a random subset of the weights, and standard
gradient descent on the remaining weights in the same mini-batch. It achieves
comparable performance to the state-of-the-art without resorting to heavy data
augmentation, using a relatively simple architecture.
A Variational Inequality Perspective on Generative Adversarial Networks
Authors: Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien
Date: 2018-02-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Misconceptions

Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend techniques designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.
Likelihood-free inference with emulator networks
Authors: Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
Date: 2018-05-23 00:00:00
Tasks: Bayesian Inference

Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on high-dimensional problems which are challenging for conventional ABC approaches.
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
Authors: Aaron Babier, Timothy C. Y. Chan, Adam Diamant, Rafid Mahmood
Date: 2018-05-23 00:00:00
Tasks: Active Learning, Portfolio Optimization, Stochastic Optimization

The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this work, we combine techniques from both fields to address the problem of learning to generate decisions to instances of continuous optimization problems where the feasible set varies with contextual features. We propose a novel framework for training a generative model to estimate optimal decisions by combining interior point methods and adversarial learning, which we further embed within an data generation algorithm. Decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Finally, we investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively.
A Universal Music Translation Network
Authors: Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman
Date: 2018-05-21 00:00:00
Tasks: Translation

We present a method for translating music across musical instruments, genres,
and styles. This method is based on a multi-domain wavenet autoencoder, with a
shared encoder and a disentangled latent space that is trained end-to-end on
waveforms. Employing a diverse training dataset and large net capacity, the
domain-independent encoder allows us to translate even from musical domains
that were not seen during training. The method is unsupervised and does not
rely on supervision in the form of matched samples between domains or musical
transcriptions. We evaluate our method on NSynth, as well as on a dataset
collected from professional musicians, and achieve convincing translations,
even when translating from whistling, potentially enabling the creation of
instrumental music by untrained humans.
On Nesting Monte Carlo Estimators
Authors: Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood
Date: 2017-09-18 00:00:00
Proceeding: ICML 2018 7
Tasks: Experimental Design

Many problems in machine learning and statistics involve nested expectations
and thus do not permit conventional Monte Carlo (MC) estimation. For such
problems, one must nest estimators, such that terms in an outer estimator
themselves involve calculation of a separate, nested, estimation. We
investigate the statistical implications of nesting MC estimators, including
cases of multiple levels of nesting, and establish the conditions under which
they converge. We derive corresponding rates of convergence and provide
empirical evidence that these rates are observed in practice. We further
establish a number of pitfalls that can arise from naive nesting of MC
estimators, provide guidelines about how these can be avoided, and lay out
novel methods for reformulating certain classes of nested expectation problems
into single expectations, leading to improved convergence rates. We demonstrate
the applicability of our work by using our results to develop a new estimator
for discrete Bayesian experimental design problems and derive error bounds for
a class of variational objectives.
Machine-learning inference of fluid variables from data using reservoir computing
Authors: Kengo Nakai, Yoshitaka Saiki
Date: 2018-05-23 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

We infer both microscopic and macroscopic behaviors of a three-dimensional
chaotic fluid flow using reservoir computing. In our procedure of the
inference, we assume no prior knowledge of a physical process of a fluid flow
except that its behavior is complex but deterministic. We present two ways of
inference of the complex behavior; the first called partial-inference requires
continued knowledge of partial time-series data during the inference as well as
past time-series data, while the second called full-inference requires only
past time-series data as training data. For the first case, we are able to
infer long-time motion of microscopic fluid variables. For the second case, we
show that the reservoir dynamics constructed from only past data of energy
functions can infer the future behavior of energy functions and reproduce the
energy spectrum. It is also shown that we can infer a time-series data from
only one measurement by using the delay coordinates. These implies that the
obtained two reservoir systems constructed without the knowledge of microscopic
data are equivalent to the dynamical systems describing macroscopic behavior of
energy functions.
On self-play computation of equilibrium in poker
Authors: Mikhail Goykhman
Date: 2018-05-23 00:00:00
Tasks: counterfactual

We compare performance of the genetic algorithm and the counterfactual regret
minimization algorithm in computing the near-equilibrium strategies in the
simplified poker games. We focus on the von Neumann poker and the simplified
version of the Texas Hold'Em poker, and test outputs of the considered
algorithms against analytical expressions defining the Nash equilibrium
strategies. We comment on the performance of the studied algorithms against
opponents deviating from equilibrium.
Variational Inference for Data-Efficient Model Learning in POMDPs
Authors: Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann
Date: 2018-05-23 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Open-Ended Question Answering, Variational Inference

Partially observable Markov decision processes (POMDPs) are a powerful
abstraction for tasks that require decision making under uncertainty, and
capture a wide range of real world tasks. Today, effective planning approaches
exist that generate effective strategies given black-box models of a POMDP
task. Yet, an open question is how to acquire accurate models for complex
domains. In this paper we propose DELIP, an approach to model learning for
POMDPs that utilizes amortized structured variational inference. We empirically
show that our model leads to effective control strategies when coupled with
state-of-the-art planners. Intuitively, model-based approaches should be
particularly beneficial in environments with changing reward structures, or
where rewards are initially unknown. Our experiments confirm that DELIP is
particularly effective in this setting.
WisenetMD: Motion Detection Using Dynamic Background Region Analysis
Authors: Sang-Ha Lee, Soon-Chul Kwon, Jin-Wook Shim, Jeong-Eun Lim, Jisang Yoo
Date: 2018-05-23 00:00:00
Tasks: Motion Detection

Motion detection algorithms that can be applied to surveillance cameras such
as CCTV (Closed Circuit Television) have been studied extensively. Motion
detection algorithm is mostly based on background subtraction. One main issue
in this technique is that false positives of dynamic backgrounds such as wind
shaking trees and flowing rivers might occur. In this paper, we proposed a
method to search for dynamic background region by analyzing the video and
removing false positives by re-checking false positives. The proposed method
was evaluated based on CDnet 2012/2014 dataset obtained at
"changedetection.net" site. We also compared its processing speed with other
algorithms.
Reinforcement Learning for Heterogeneous Teams with PALO Bounds
Authors: Roi Ceren, Prashant Doshi, Keyang He
Date: 2018-05-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce reinforcement learning for heterogeneous teams in which rewards
for an agent are additively factored into local costs, stimuli unique to each
agent, and global rewards, those shared by all agents in the domain. Motivating
domains include coordination of varied robotic platforms, which incur different
costs for the same action, but share an overall goal. We present two templates
for learning in this setting with factored rewards: a generalization of
Perkins' Monte Carlo exploring starts for POMDPs to canonical MPOMDPs, with a
single policy mapping joint observations of all agents to joint actions
(MCES-MP); and another with each agent individually mapping joint observations
to their own action (MCES-FMP). We use probably approximately local optimal
(PALO) bounds to analyze sample complexity, instantiating these templates to
PALO learning. We promote sample efficiency by including a policy space pruning
technique, and evaluate the approaches on three domains of heterogeneous agents
demonstrating that MCES-FMP yields improved policies in less samples compared
to MCES-MP and a previous benchmark.
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Authors: Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan How
Date: 2018-05-23 00:00:00
Tasks: Gaussian Processes

Distributed machine learning (ML) is a modern computation paradigm that
divides its workload into independent tasks that can be simultaneously achieved
by multiple machines (i.e., agents) for better scalability. However, a typical
distributed system is usually implemented with a central server that collects
data statistics from multiple independent machines operating on different
subsets of data to build a global analytic model. This centralized
communication architecture however exposes a single choke point for operational
failure and places severe bottlenecks on the server's communication and
computation capacities as it has to process a growing volume of communication
from a crowd of learning agents. To mitigate these bottlenecks, this paper
introduces a novel Collective Online Learning Gaussian Process framework for
massive distributed systems that allows each agent to build its local model,
which can be exchanged and combined efficiently with others via peer-to-peer
communication to converge on a global model of higher quality. Finally, our
empirical results consistently demonstrate the efficiency of our framework on
both synthetic and real-world datasets.
Learning Illuminant Estimation from Object Recognition
Authors: Marco Buzzelli, Joost Van de Weijer, Raimondo Schettini
Date: 2018-05-23 00:00:00
Tasks: Color Constancy, Deep Learning, Object, Object Recognition

In this paper we present a deep learning method to estimate the illuminant of
an image. Our model is not trained with illuminant annotations, but with the
objective of improving performance on an auxiliary task such as object
recognition. To the best of our knowledge, this is the first example of a deep
learning architecture for illuminant estimation that is trained without ground
truth illuminants. We evaluate our solution on standard datasets for color
constancy, and compare it with state of the art methods. Our proposal is shown
to outperform most deep learning methods in a cross-dataset evaluation setup,
and to present competitive results in a comparison with parametric solutions.
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
Authors: Zhaobin Mo, Sisi Li, Diange Yang, Ding Zhao
Date: 2018-02-27 00:00:00
Tasks: Clustering, Dynamic Time Warping

It is necessary to thoroughly evaluate the effectiveness and safety of
Connected Vehicles (CVs) algorithm before their release and deployment. Current
evaluation approach mainly relies on simulation platform with the
single-vehicle driving model. The main drawback of it is the lack of network
realism. To overcome this problem, we extract naturalistic V2V encounters data
from the database, and then separate the primary vehicle encounter category by
clustering. A fast mining algorithm is proposed that can be applied to parallel
query for further process acceleration. 4,500 encounters are mined from a 275
GB database collected in the Safety Pilot Model Program in Ann Arbor Michigan,
USA. K-means and Dynamic Time Warping (DTW) are used in clustering. Results
show this method can quickly mine and cluster primary driving scenarios from a
large database. Our results separate the car-following, intersection and
by-passing, which are the primary category of the vehicle encounter. We
anticipate the work in the essay can become a general method to effectively
extract vehicle encounters from any existing database that contains vehicular
GPS information. What's more, the naturalistic data of different vehicle
encounters can be applied in Connected Vehicles evaluation.
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Authors: Tor Lattimore, Csaba Szepesvari
Date: 2018-05-23 00:00:00
Tasks: General Classification

Partial monitoring is a generalization of the well-known multi-armed bandit
framework where the loss is not directly observed by the learner. We complete
the classification of finite adversarial partial monitoring to include all
games, solving an open problem posed by Bartok et al. [2014]. Along the way we
simplify and improve existing algorithms and correct errors in previous
analyses. Our second contribution is a new algorithm for the class of games
studied by Bartok [2013] where we prove upper and lower regret bounds that shed
more light on the dependence of the regret on the game structure.
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs
Authors: Davide Bacciu, Andrea Bongiorno
Date: 2018-05-23 00:00:00

The paper introduces concentric Echo State Network, an approach to design
reservoir topologies that tries to bridge the gap between deterministically
constructed simple cycle models and deep reservoir computing approaches. We
show how to modularize the reservoir into simple unidirectional and concentric
cycles with pairwise bidirectional jump connections between adjacent loops. We
provide a preliminary experimental assessment showing how concentric reservoirs
yield to superior predictive accuracy and memory capacity with respect to
single cycle reservoirs and deep reservoir models.