pwc-archive/papers-with-abstracts huggingface dataset:
Discriminatory Transfer
Authors: Chao Lan, Jun Huan
Date: 2017-07-03 00:00:00
Tasks: Fairness, Multi-Task Learning, Prediction, Transfer Learning

We observe standard transfer learning can improve prediction accuracies of
target tasks at the cost of lowering their prediction fairness -- a phenomenon
we named discriminatory transfer. We examine prediction fairness of a standard
hypothesis transfer algorithm and a standard multi-task learning algorithm, and
show they both suffer discriminatory transfer on the real-world Communities and
Crime data set. The presented case study introduces an interaction between
fairness and transfer learning, as an extension of existing fairness studies
that focus on single task learning.
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
Authors: Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran
Date: 2017-03-20 00:00:00

Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics of training data. To this end, we evaluate
CNNs on negative images, since they share the same structure and semantics as
regular images and humans can classify them correctly. Our experimental results
indicate that when training on regular images and testing on negative images,
the model accuracy is significantly lower than when it is tested on regular
images. This leads us to the conjecture that current training methods do not
effectively train models to generalize the concepts. We then introduce the
notion of semantic adversarial examples - transformed inputs that semantically
represent the same objects, but the model does not classify them correctly -
and present negative images as one class of such inputs.
Reinforced Video Captioning with Entailment Rewards
Authors: Ramakanth Pasunuru, Mohit Bansal
Date: 2017-08-07 00:00:00
Proceeding: EMNLP 2017 9
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Video Captioning

Sequence-to-sequence models have shown promising improvements on the temporal
task of video captioning, but they optimize word-level cross-entropy loss
during training. First, using policy gradient and mixed-loss methods for
reinforcement learning, we directly optimize sentence-level task-based metrics
(as rewards), achieving significant improvements over the baseline, based on
both automatic metrics and human evaluation on multiple datasets. Next, we
propose a novel entailment-enhanced reward (CIDEnt) that corrects
phrase-matching based metrics (such as CIDEr) to only allow for
logically-implied partial matches and avoid contradictions, achieving further
significant improvements over the CIDEr-reward model. Overall, our
CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Authors: Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III
Date: 2017-08-03 00:00:00
Proceeding: WS 2017 9
Tasks: Domain Adaptation, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Translation

We describe the University of Maryland machine translation systems submitted
to the WMT17 German-English Bandit Learning Task. The task is to adapt a
translation system to a new domain, using only bandit feedback: the system
receives a German sentence to translate, produces an English sentence, and only
gets a scalar score as feedback. Targeting these two challenges (adaptation and
bandit learning), we built a standard neural machine translation system and
extended it in two ways: (1) robust reinforcement learning techniques to learn
effectively from the bandit feedback, and (2) domain adaptation using data
selection from a large corpus of parallel data.
RoomNet: End-to-End Room Layout Estimation
Authors: Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz, Andrew Rabinovich
Date: 2017-03-18 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder, Room Layout Estimation, Segmentation, Semantic Segmentation

This paper focuses on the task of room layout estimation from a monocular RGB
image. Prior works break the problem into two sub-tasks: semantic segmentation
of floor, walls, ceiling to produce layout hypotheses, followed by an iterative
optimization step to rank these hypotheses. In contrast, we adopt a more direct
formulation of this problem as one of estimating an ordered set of room layout
keypoints. The room layout and the corresponding segmentation is completely
specified given the locations of these ordered keypoints. We predict the
locations of the room layout keypoints using RoomNet, an end-to-end trainable
encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN,
we achieve state-of-the-art performance along with 200x to 600x speedup
compared to the most recent work. Additionally, we present optional extensions
to the RoomNet architecture such as including recurrent computations and memory
units to refine the keypoint locations under the same parametric capacity.
On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics
Authors: Chao Lan, Sai Nivedita Chandrasekaran, Jun Huan
Date: 2017-04-03 00:00:00

In cheminformatics, compound-target binding profiles has been a main source
of data for research. For data repositories that only provide positive
profiles, a popular assumption is that unreported profiles are all negative. In
this paper, we caution audience not to take this assumption for granted, and
present empirical evidence of its ineffectiveness from a machine learning
perspective. Our examination is based on a setting where binding profiles are
used as features to train predictive models; we show (1) prediction performance
degrades when the assumption fails and (2) explicit recovery of unreported
profiles improves prediction performance. In particular, we propose a framework
that jointly recovers profiles and learns predictive model, and show it
achieves further performance improvement. The presented study not only suggests
applying matrix recovery methods to recover unreported profiles, but also
initiates a new missing feature problem which we called Learning with Positive
and Unknown Features.
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
Authors: Ali Ahmadvand, Jinho D. Choi
Date: 2017-08-07 00:00:00
Tasks: Answer Selection, Multi-Task Learning, Question Answering, Transfer Learning

Transferring knowledge from a source domain to another domain is useful, especially when gathering new data is very expensive and time-consuming. Deep networks have been well-studied for question answering tasks in recent years; however, no prominent research for transfer learning through deep neural networks exists in the question answering field. In this paper, two main methods (INIT and MULT) in this field are examined. Then, a new method named Intelligent sample selection (ISS-MULT) is proposed to improve the MULT method for question answering tasks. Different datasets, specificay SQuAD, SelQA, WikiQA, NewWikiQA and InforBoxQA, are used for evaluation. Moreover, two different tasks of question answering - answer selection and answer triggering - are evaluated to examine the effectiveness of transfer learning. The results show that using transfer learning generally improves the performance if the corpora are related and are based on the same policy. In addition, using ISS-MULT could finely improve the MULT method for question answering tasks, and these improvements prove more significant in the answer triggering task.
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
Authors: Maxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox
Date: 2017-03-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Decoder, Vocal Bursts Intensity Prediction

We present a deep convolutional decoder architecture that can generate
volumetric 3D outputs in a compute- and memory-efficient manner by using an
octree representation. The network learns to predict both the structure of the
octree, and the occupancy values of individual cells. This makes it a
particularly valuable technique for generating 3D shapes. In contrast to
standard decoders acting on regular voxel grids, the architecture does not have
cubic complexity. This allows representing much higher resolution outputs with
a limited memory budget. We demonstrate this in several application domains,
including 3D convolutional autoencoders, generation of objects and whole scenes
from high-level representations, and shape from a single image.
Asking Too Much? The Rhetorical Role of Questions in Political Discourse
Authors: Justine Zhang, Arthur Spirling, Cristian Danescu-Niculescu-Mizil
Date: 2017-08-07 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering

Questions play a prominent role in social interactions, performing rhetorical
functions that go beyond that of simple informational exchange. The surface
form of a question can signal the intention and background of the person asking
it, as well as the nature of their relation with the interlocutor. While the
informational nature of questions has been extensively examined in the context
of question-answering applications, their rhetorical aspects have been largely
understudied.
  In this work we introduce an unsupervised methodology for extracting surface
motifs that recur in questions, and for grouping them according to their latent
rhetorical role. By applying this framework to the setting of question sessions
in the UK parliament, we show that the resulting typology encodes key aspects
of the political discourse---such as the bifurcation in questioning behavior
between government and opposition parties---and reveals new insights into the
effects of a legislator's tenure and political career ambitions.
Delayed acceptance ABC-SMC
Authors: Richard G. Everitt, Paulina A. Rowińska
Date: 2017-08-07 00:00:00

Approximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available. It relies on the use of a~model that is specified in the form of a~simulator, and approximates the likelihood at a~parameter value $\theta$ by simulating auxiliary data sets $x$ and evaluating the distance of $x$ from the true data $y$. However, ABC is not computationally feasible in cases where using the simulator for each $\theta$ is very expensive. This paper investigates this situation in cases where a~cheap, but approximate, simulator is available. The approach is to employ delayed acceptance Markov chain Monte Carlo (MCMC) within an ABC sequential Monte Carlo (SMC) sampler in order to, in a~first stage of the kernel, use the cheap simulator to rule out parts of the parameter space that are not worth exploring, so that the ``true'' simulator is only run (in the second stage of the kernel) where there is a~reasonable chance of accepting proposed values of $\theta$. We show that this approach can be used quite automatically, with few tuning parameters. Applications to stochastic differential equation models and latent doubly intractable distributions are presented.
Stochastic Games for Smart Grid Energy Management with Prospect Prosumers
Authors: Seyed Rasoul Etesami, Walid Saad, Narayan Mandayam, H. Vincent Poor
Date: 2016-10-06 00:00:00
Tasks: energy management, Management

In this paper, the problem of smart grid energy management under stochastic
dynamics is investigated. In the considered model, at the demand side, it is
assumed that customers can act as prosumers who own renewable energy sources
and can both produce and consume energy. Due to the coupling between the
prosumers' decisions and the stochastic nature of renewable energy, the
interaction among prosumers is formulated as a stochastic game, in which each
prosumer seeks to maximize its payoff, in terms of revenues, by controlling its
energy consumption and demand. In particular, the subjective behavior of
prosumers is explicitly reflected into their payoff functions using prospect
theory, a powerful framework that allows modeling real-life human choices. For
this prospect-based stochastic game, it is shown that there always exists a
stationary Nash equilibrium where the prosumers' trading policies in the
equilibrium are independent of the time and their histories of the play.
Moreover, a novel distributed algorithm with no information sharing among
prosumers is proposed and shown to converge to an $\epsilon$-Nash equilibrium.
On the other hand, at the supply side, the interaction between the utility
company and the prosumers is formulated as an online optimization problem in
which the utility company's goal is to learn its optimal energy allocation
rules. For this case, it is shown that such an optimization problem admits a
no-regret algorithm meaning that regardless of the actual outcome of the game
among the prosumers, the utility company can follow a strategy that mitigates
its allocation costs as if it knew the entire demand market a priori.
Simulation results show the convergence of the proposed algorithms to their
predicted outcomes and present new insights resulting from prospect theory that
contribute toward more efficient energy management in the smart grids.
Training Deep Networks to be Spatially Sensitive
Authors: Nicholas Kolkin, Gregory Shakhnarovich, Eli Shechtman
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Saliency Prediction, Semantic Segmentation

In many computer vision tasks, for example saliency prediction or semantic
segmentation, the desired output is a foreground map that predicts pixels where
some criteria is satisfied. Despite the inherently spatial nature of this task
commonly used learning objectives do not incorporate the spatial relationships
between misclassified pixels and the underlying ground truth. The Weighted
F-measure, a recently proposed evaluation metric, does reweight errors
spatially, and has been shown to closely correlate with human evaluation of
quality, and stably rank predictions with respect to noisy ground truths (such
as a sloppy human annotator might generate). However it suffers from
computational complexity which makes it intractable as an optimization
objective for gradient descent, which must be evaluated thousands or millions
of times while learning a model's parameters. We propose a differentiable and
efficient approximation of this metric. By incorporating spatial information
into the objective we can use a simpler model than competing methods without
sacrificing accuracy, resulting in faster inference speeds and alleviating the
need for pre/post-processing. We match (or improve) performance on several
tasks compared to prior state of the art by traditional metrics, and in many
cases significantly improve performance by the weighted F-measure.
Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments
Authors: Qing Ping, Chaomei Chen
Date: 2017-08-07 00:00:00
Tasks: Highlight Detection

With the prevalence of video sharing, there are increasing demands for
automatic video digestion such as highlight detection. Recently, platforms with
crowdsourced time-sync video comments have emerged worldwide, providing a good
opportunity for highlight detection. However, this task is non-trivial: (1)
time-sync comments often lag behind their corresponding shot; (2) time-sync
comments are semantically sparse and noisy; (3) to determine which shots are
highlights is highly subjective. The present paper aims to tackle these
challenges by proposing a framework that (1) uses concept-mapped lexical-chains
for lag calibration; (2) models video highlights based on comment intensity and
combination of emotion and concept concentration of each shot; (3) summarize
each detected highlight using improved SumBasic with emotion and concept
mapping. Experiments on large real-world datasets show that our highlight
detection method and summarization method both outperform other benchmarks with
considerable margins.
MemNet: A Persistent Memory Network for Image Restoration
Authors: Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Color Image Denoising, Image Restoration, Image Super-Resolution, JPEG Artifact Correction, Super-Resolution

Recently, very deep convolutional neural networks (CNNs) have been attracting
considerable attention in image restoration. However, as the depth grows, the
long-term dependency problem is rarely realized for these very deep models,
which results in the prior states/layers having little influence on the
subsequent ones. Motivated by the fact that human thoughts have persistency, we
propose a very deep persistent memory network (MemNet) that introduces a memory
block, consisting of a recursive unit and a gate unit, to explicitly mine
persistent memory through an adaptive learning process. The recursive unit
learns multi-level representations of the current state under different
receptive fields. The representations and the outputs from the previous memory
blocks are concatenated and sent to the gate unit, which adaptively controls
how much of the previous states should be reserved, and decides how much of the
current state should be stored. We apply MemNet to three image restoration
tasks, i.e., image denosing, super-resolution and JPEG deblocking.
Comprehensive experiments demonstrate the necessity of the MemNet and its
unanimous superiority on all three tasks over the state of the arts. Code is
available at https://github.com/tyshiwo/MemNet.
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Authors: Kihyuk Sohn, Sifei Liu, Guangyu Zhong, Xiang Yu, Ming-Hsuan Yang, Manmohan Chandraker
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Data Augmentation, Domain Adaptation, Face Recognition, Unsupervised Domain Adaptation

Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses both of those challenges, through an image to video feature-level
domain adaptation approach, to learn discriminative video frame
representations. The framework utilizes large-scale unlabeled video data to
reduce the gap between different domains while transferring discriminative
knowledge from large-scale labeled still images. Given a face recognition
network that is pretrained in the image domain, the adaptation is achieved by
(i) distilling knowledge from the network to a video adaptation network through
feature matching, (ii) performing feature restoration through synthetic data
augmentation and (iii) learning a domain-invariant feature through a domain
adversarial discriminator. We further improve performance through a
discriminator-guided feature fusion that boosts high-quality frames while
eliminating those degraded by video domain-specific factors. Experiments on the
YouTube Faces and IJB-A datasets demonstrate that each module contributes to
our feature-level domain adaptation framework and substantially improves video
face recognition performance to achieve state-of-the-art accuracy. We
demonstrate qualitatively that the network learns to suppress diverse artifacts
in videos such as pose, illumination or occlusion without being explicitly
trained for them.
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Authors: Sébastien Forestier, Rémy Portelas, Yoan Mollard, Pierre-Yves Oudeyer
Date: 2017-08-07 00:00:00
Tasks: Developmental Learning, Multi-Goal Reinforcement Learning, Reinforcement Learning, Self-Supervised Learning

Intrinsically motivated spontaneous exploration is a key enabler of autonomous developmental learning in human children. It enables the discovery of skill repertoires through autotelic learning, i.e. the self-generation, self-selection, self-ordering and self-experimentation of learning goals. We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous learning in machines. The IMGEP architecture relies on several principles: 1) self-generation of goals, generalized as parameterized fitness functions; 2) selection of goals based on intrinsic rewards; 3) exploration with incremental goal-parameterized policy search and exploitation with a batch learning algorithm; 4) systematic reuse of information acquired when targeting a goal for improving towards other goals. We present a particularly efficient form of IMGEP, called AMB, that uses a population-based policy and an object-centered spatio-temporal modularity. We provide several implementations of this architecture and demonstrate their ability to automatically generate a learning curriculum within several experimental setups. One of these experiments includes a real humanoid robot exploring multiple spaces of goals with several hundred continuous dimensions and with distractors. While no particular target goal is provided to these autotelic agents, this curriculum allows the discovery of diverse skills that act as stepping stones for learning more complex skills, e.g. nested tool use.
PowerAI DDL
Authors: Minsik Cho, Ulrich Finkler, Sameer Kumar, David Kung, Vaibhav Saxena, Dheeraj Sreedhar
Date: 2017-08-07 00:00:00
Tasks: GPU

As deep neural networks become more complex and input datasets grow larger,
it can take days or even weeks to train a deep neural network to the desired
accuracy. Therefore, distributed Deep Learning at a massive scale is a critical
capability, since it offers the potential to reduce the training time from
weeks to hours. In this paper, we present a software-hardware co-optimized
distributed Deep Learning system that can achieve near-linear scaling up to
hundreds of GPUs. The core algorithm is a multi-ring communication pattern that
provides a good tradeoff between latency and bandwidth and adapts to a variety
of system configurations. The communication algorithm is implemented as a
library for easy use. This library has been integrated into Tensorflow, Caffe,
and Torch. We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC
servers (256 GPUs) in about 7 hours to an accuracy of 33.8 % validation
accuracy. Microsoft's ADAM and Google's DistBelief results did not reach 30 %
validation accuracy for Imagenet 22K. Compared to Facebook AI Research's recent
paper on 256 GPU training, we use a different communication algorithm, and our
combined software and hardware system offers better communication overhead for
Resnet-50. A PowerAI DDL enabled version of Torch completed 90 epochs of
training on Resnet 50 for 1K classes in 50 minutes using 64 IBM Power8 S822LC
servers (256 GPUs).