pwc-archive/papers-with-abstracts huggingface dataset:
Automated Mechanism Design via Neural Networks
Authors: Weiran Shen, Pingzhong Tang, Song Zuo
Date: 2018-05-09 00:00:00

Using AI approaches to automatically design mechanisms has been a central research mission at the interface of AI and economics [Conitzer and Sandholm, 2002]. Previous approaches that attempt to design revenue optimal auctions for the multi-dimensional settings fall short in at least one of the three aspects: 1) representation -- search in a space that probably does not even contain the optimal mechanism; 2) exactness -- finding a mechanism that is either not truthful or far from optimal; 3) domain dependence -- need a different design for different environment settings. To resolve the three difficulties, in this paper, we put forward -- MenuNet -- a unified neural network based framework that automatically learns to design revenue optimal mechanisms. Our framework consists of a mechanism network that takes an input distribution for training and outputs a mechanism, as well as a buyer network that takes a mechanism as input and output an action. Such a separation in design mitigates the difficulty to impose incentive compatibility constraints on the mechanism, by making it a rational choice of the buyer. As a result, our framework easily overcomes the previously mentioned difficulty in incorporating IC constraints and always returns exactly incentive compatible mechanisms. We then apply our framework to a number of multi-item revenue optimal design settings, for a few of which the theoretically optimal mechanisms are unknown. We then go on to theoretically prove that the mechanisms found by our framework are indeed optimal. To the best of our knowledge, we are the first to apply neural networks to discover optimal auction mechanisms with provable optimality.
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Authors: Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, Chaoran Huang, Xiaodong Ning
Date: 2018-05-09 00:00:00
Tasks: Fraud Detection

Online reviews play an important role in influencing buyers' daily purchase
decisions. However, fake and meaningless reviews, which cannot reflect users'
genuine purchase experience and opinions, widely exist on the Web and pose
great challenges for users to make right choices. Therefore,it is desirable to
build a fair model that evaluates the quality of products by distinguishing
spamming reviews. We present an end-to-end trainable unified model to leverage
the appealing properties from Autoencoder and random forest. A stochastic
decision tree model is implemented to guide the global parameter learning
process. Extensive experiments were conducted on a large Amazon review dataset.
The proposed model consistently outperforms a series of compared methods.
Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task
Authors: Zakaria Aldeneh, Dimitrios Dimitriadis, Emily Mower Provost
Date: 2018-05-09 00:00:00

This work focuses on the use of acoustic cues for modeling turn-taking in
dyadic spoken dialogues. Previous work has shown that speaker intentions (e.g.,
asking a question, uttering a backchannel, etc.) can influence turn-taking
behavior and are good predictors of turn-transitions in spoken dialogues.
However, speaker intentions are not readily available for use by automated
systems at run-time; making it difficult to use this information to anticipate
a turn-transition. To this end, we propose a multi-task neural approach for
predicting turn- transitions and speaker intentions simultaneously. Our results
show that adding the auxiliary task of speaker intention prediction improves
the performance of turn-transition prediction in spoken dialogues, without
relying on additional input features during run-time.
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Authors: Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen
Date: 2018-05-01 00:00:00
Tasks: Knowledge Distillation, Retrieval, Text Matching

Intelligent personal assistant systems with either text-based or voice-based
conversational interfaces are becoming increasingly popular around the world.
Retrieval-based conversation models have the advantages of returning fluent and
informative responses. Most existing studies in this area are on open domain
"chit-chat" conversations or task / transaction oriented conversations. More
research is needed for information-seeking conversations. There is also a lack
of modeling external knowledge beyond the dialog utterances among current
conversational models. In this paper, we propose a learning framework on the
top of deep neural matching networks that leverages external knowledge for
response ranking in information-seeking conversation systems. We incorporate
external knowledge into deep neural models with pseudo-relevance feedback and
QA correspondence knowledge distillation. Extensive experiments with three
information-seeking conversation data sets including both open benchmarks and
commercial data show that, our methods outperform various baseline methods
including several deep text matching models and the state-of-the-art method on
response selection in multi-turn conversations. We also perform analysis over
different response types, model variations and ranking examples. Our models and
research findings provide new insights on how to utilize external knowledge
with deep neural models for response selection and have implications for the
design of the next generation of information-seeking conversation systems.
Learning to Teach
Authors: Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, Tie-Yan Liu
Date: 2018-05-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Reinforcement Learning

Teaching plays a very important role in our society, by spreading human
knowledge and educating our next generations. A good teacher will select
appropriate teaching materials, impact suitable methodologies, and set up
targeted examinations, according to the learning behaviors of the students. In
the field of artificial intelligence, however, one has not fully explored the
role of teaching, and pays most attention to machine \emph{learning}. In this
paper, we argue that equal attention, if not more, should be paid to teaching,
and furthermore, an optimization framework (instead of heuristics) should be
used to obtain good teaching strategies. We call this approach `learning to
teach'. In the approach, two intelligent agents interact with each other: a
student model (which corresponds to the learner in traditional machine learning
algorithms), and a teacher model (which determines the appropriate data, loss
function, and hypothesis space to facilitate the training of the student
model). The teacher model leverages the feedback from the student model to
optimize its own teaching strategies by means of reinforcement learning, so as
to achieve teacher-student co-evolution. To demonstrate the practical value of
our proposed approach, we take the training of deep neural networks (DNN) as an
example, and show that by using the learning to teach techniques, we are able
to use much less training data and fewer iterations to achieve almost the same
accuracy for different kinds of DNN models (e.g., multi-layer perceptron,
convolutional neural networks and recurrent neural networks) under various
machine learning tasks (e.g., image classification and text understanding).
LearningWord Embeddings for Low-resource Languages by PU Learning
Authors: Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang
Date: 2018-05-09 00:00:00

Word embedding is a key component in many downstream applications in
processing natural languages. Existing approaches often assume the existence of
a large collection of text for learning effective word embedding. However, such
a corpus may not be available for some low-resource languages. In this paper,
we study how to effectively learn a word embedding model on a corpus with only
a few million tokens. In such a situation, the co-occurrence matrix is sparse
as the co-occurrences of many word pairs are unobserved. In contrast to
existing approaches often only sample a few unobserved word pairs as negative
samples, we argue that the zero entries in the co-occurrence matrix also
provide valuable information. We then design a Positive-Unlabeled Learning
(PU-Learning) approach to factorize the co-occurrence matrix and validate the
proposed approaches in four different languages.
A Symbolic Approach to Explaining Bayesian Network Classifiers
Authors: Andy Shih, Arthur Choi, Adnan Darwiche
Date: 2018-05-09 00:00:00
Tasks: General Classification

We propose an approach for explaining Bayesian network classifiers, which is
based on compiling such classifiers into decision functions that have a
tractable and symbolic form. We introduce two types of explanations for why a
classifier may have classified an instance positively or negatively and suggest
algorithms for computing these explanations. The first type of explanation
identifies a minimal set of the currently active features that is responsible
for the current classification, while the second type of explanation identifies
a minimal set of features whose current state (active or not) is sufficient for
the classification. We consider in particular the compilation of Naive and
Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs),
providing a context for evaluating our proposal using case studies and
experiments based on classifiers from the literature.
Anchor Cascade for Efficient Face Detection
Authors: Baosheng Yu, DaCheng Tao
Date: 2018-05-09 00:00:00
Tasks: Face Detection, Face Recognition, image-classification, Image Classification

Face detection is essential to facial analysis tasks such as facial
reenactment and face recognition. Both cascade face detectors and anchor-based
face detectors have translated shining demos into practice and received
intensive attention from the community. However, cascade face detectors often
suffer from a low detection accuracy, while anchor-based face detectors rely
heavily on very large networks pre-trained on large scale image classification
datasets such as ImageNet [1], which is not computationally efficient for both
training and deployment. In this paper, we devise an efficient anchor-based
cascade framework called anchor cascade. To improve the detection accuracy by
exploring contextual information, we further propose a context pyramid maxout
mechanism for anchor cascade. As a result, anchor cascade can train very
efficient face detection models with a high detection accuracy. Specifically,
comparing with a popular CNN-based cascade face detector MTCNN [2], our anchor
cascade face detector greatly improves the detection accuracy, e.g., from
0.9435 to 0.9704 at 1k false positives on FDDB, while it still runs in
comparable speed. Experimental results on two widely used face detection
benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed
framework.
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives
Authors: Yue Wang, Swarat Chaudhuri, Lydia E. Kavraki
Date: 2018-01-29 00:00:00
Tasks: valid

Planning robust executions under uncertainty is a fundamental challenge for
building autonomous robots. Partially Observable Markov Decision Processes
(POMDPs) provide a standard framework for modeling uncertainty in many
applications. In this work, we study POMDPs with safe-reachability objectives,
which require that with a probability above some threshold, a goal state is
eventually reached while keeping the probability of visiting unsafe states
below some threshold. This POMDP formulation is different from the traditional
POMDP models with optimality objectives and we show that in some cases, POMDPs
with safe-reachability objectives can provide a better guarantee of both safety
and reachability than the existing POMDP models through an example. A key
algorithmic problem for POMDPs is policy synthesis, which requires reasoning
over a vast space of beliefs (probability distributions). To address this
challenge, we introduce the notion of a goal-constrained belief space, which
only contains beliefs reachable from the initial belief under desired
executions that can achieve the given safe-reachability objective. Our method
compactly represents this space over a bounded horizon using symbolic
constraints, and employs an incremental Satisfiability Modulo Theories (SMT)
solver to efficiently search for a valid policy over it. We evaluate our method
using a case study involving a partially observable robotic domain with
uncertain obstacles. The results show that our method can synthesize policies
over large belief spaces with a small number of SMT solver calls by focusing on
the goal-constrained belief space.
Full explicit consistency constraints in uncalibrated multiple homography estimation
Authors: Wojciech Chojnacki, Zygmunt L. Szpak
Date: 2018-05-07 00:00:00
Tasks: Homography Estimation

We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views. Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task.
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
Authors: Joshua Romoff, Peter Henderson, Alexandre Piché, Vincent Francois-Lavet, Joelle Pineau
Date: 2018-05-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) agents require the specification of a reward
signal for learning behaviours. However, introduction of corrupt or stochastic
rewards can yield high variance in learning. Such corruption may be a direct
result of goal misspecification, randomness in the reward signal, or
correlation of the reward with external factors that are not known to the
agent. Corruption or stochasticity of the reward signal can be especially
problematic in robotics, where goal specification can be particularly difficult
for complex tasks. While many variance reduction techniques have been studied
to improve the robustness of the RL process, handling such stochastic or
corrupted reward structures remains difficult. As an alternative for handling
this scenario in model-free RL methods, we suggest using an estimator for both
rewards and value functions. We demonstrate that this improves performance
under corrupted stochastic rewards in both the tabular and non-linear function
approximation settings for a variety of noise types and environments. The use
of reward estimation is a robust and easy-to-implement improvement for handling
corrupted reward signals in model-free RL.
Multi-shot Pedestrian Re-identification via Sequential Decision Making
Authors: Jianfu Zhang, Naiyan Wang, Liqing Zhang
Date: 2017-12-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decision Making, Reinforcement Learning, Sequential Decision Making, Time Series, Time Series Analysis

Multi-shot pedestrian re-identification problem is at the core of
surveillance video analysis. It matches two tracks of pedestrians from
different cameras. In contrary to existing works that aggregate single frames
features by time series model such as recurrent neural network, in this paper,
we propose an interpretable reinforcement learning based approach to this
problem. Particularly, we train an agent to verify a pair of images at each
time. The agent could choose to output the result (same or different) or
request another pair of images to verify (unsure). By this way, our model
implicitly learns the difficulty of image pairs, and postpone the decision when
the model does not accumulate enough evidence. Moreover, by adjusting the
reward for unsure action, we can easily trade off between speed and accuracy.
In three open benchmarks, our method are competitive with the state-of-the-art
methods while only using 3% to 6% images. These promising results demonstrate
that our method is favorable in both efficiency and performance.
Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
Authors: Jinquan Sun, Yinghuan Shi, Yang Gao, Lei Wang, Luping Zhou, Wanqi Yang, Dinggang Shen
Date: 2018-04-27 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Due to low tissue contrast, irregular object appearance, and unpredictable
location variation, segmenting the objects from different medical imaging
modalities (e.g., CT, MR) is considered as an important yet challenging task.
In this paper, we present a novel method for interactive medical image
segmentation with the following merits. (1) Our design is fundamentally
different from previous pure patch-based and image-based segmentation methods.
We observe that during delineation, the physician repeatedly check the
inside-outside intensity changing to determine the boundary, which indicates
that comparison in an inside-outside manner is extremely important. Thus, we
innovatively model our segmentation task as learning the representation of the
bi-directional sequential patches, starting from (or ending in) the given
central point of the object. This can be realized by our proposed ConvRNN
network embedded with a gated memory propagation unit. (2) Unlike previous
interactive methods (requiring bounding box or seed points), we only ask the
physician to merely click on the rough central point of the object before
segmentation, which could simultaneously enhance the performance and reduce the
segmentation time. (3) We utilize our method in a multi-level framework for
better performance. We systematically evaluate our method in three different
segmentation tasks including CT kidney tumor, MR prostate, and PROMISE12
challenge, showing promising results compared with state-of-the-art methods.
The code is available here:
\href{https://github.com/sunalbert/Sequential-patch-based-segmentation}{Sequential-patch-based-segmentation}.
A Memristor based Unsupervised Neuromorphic System Towards Fast and Energy-Efficient GAN
Authors: F. Liu, C. Liu, F. Bi
Date: 2018-05-09 00:00:00
Tasks: Generative Adversarial Network, GPU

Deep Learning has gained immense success in pushing today's artificial intelligence forward. To solve the challenge of limited labeled data in the supervised learning world, unsupervised learning has been proposed years ago while low accuracy hinters its realistic applications. Generative adversarial network (GAN) emerges as an unsupervised learning approach with promising accuracy and are under extensively study. However, the execution of GAN is extremely memory and computation intensive and results in ultra-low speed and high-power consumption. In this work, we proposed a holistic solution for fast and energy-efficient GAN computation through a memristor-based neuromorphic system. First, we exploited a hardware and software co-design approach to map the computation blocks in GAN efficiently. We also proposed an efficient data flow for optimal parallelism training and testing, depending on the computation correlations between different computing blocks. To compute the unique and complex loss of GAN, we developed a diff-block with optimized accuracy and performance. The experiment results on big data show that our design achieves 2.8x speedup and 6.1x energy-saving compared with the traditional GPU accelerator, as well as 5.5x speedup and 1.4x energy-saving compared with the previous FPGA-based accelerator.
Decoupling Dynamics and Reward for Transfer Learning
Authors: Amy Zhang, Harsh Satija, Joelle Pineau
Date: 2018-04-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Current reinforcement learning (RL) methods can successfully learn single
tasks but often generalize poorly to modest perturbations in task domain or
training procedure. In this work, we present a decoupled learning strategy for
RL that creates a shared representation space where knowledge can be robustly
transferred. We separate learning the task representation, the forward
dynamics, the inverse dynamics and the reward function of the domain, and show
that this decoupling improves performance within the task, transfers well to
changes in dynamics and reward, and can be effectively used for online
planning. Empirical results show good performance in both continuous and
discrete RL domains.
Transferable Semi-supervised Semantic Segmentation
Authors: Huaxin Xiao, Yunchao Wei, Yu Liu, Maojun Zhang, Jiashi Feng
Date: 2017-11-18 00:00:00
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

The performance of deep learning based semantic segmentation models heavily
depends on sufficient data with careful annotations. However, even the largest
public datasets only provide samples with pixel-level annotations for rather
limited semantic categories. Such data scarcity critically limits scalability
and applicability of semantic segmentation models in real applications. In this
paper, we propose a novel transferable semi-supervised semantic segmentation
model that can transfer the learned segmentation knowledge from a few strong
categories with pixel-level annotations to unseen weak categories with only
image-level annotations, significantly broadening the applicable territory of
deep segmentation models. In particular, the proposed model consists of two
complementary and learnable components: a Label transfer Network (L-Net) and a
Prediction transfer Network (P-Net). The L-Net learns to transfer the
segmentation knowledge from strong categories to the images in the weak
categories and produces coarse pixel-level semantic maps, by effectively
exploiting the similar appearance shared across categories. Meanwhile, the
P-Net tailors the transferred knowledge through a carefully designed
adversarial learning strategy and produces refined segmentation results with
better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4%
performance of the fully-supervised baseline using 50% and 0% categories with
pixel-level annotations respectively on PASCAL VOC 2012. With such a novel
transfer mechanism, our proposed model is easily generalizable to a variety of
new categories, only requiring image-level annotations, and offers appealing
scalability in real applications.
Multi-scale metrics and self-organizing maps: a computational approach to the structure of sensory maps
Authors: William H. Wilson
Date: 2018-05-09 00:00:00

This paper introduces the concept of a bi-scale metric for use in the
cooperative phase of the self-organizing map (SOM) algorithm. Use of a bi-scale
metric allows segmentation of the map into a number of regions, corresponding
to anticipated cluster structure in the data. Such a situation occurs, for
example, in the somatotopic maps which inspired the SOM algo- rithm, where
clusters of data may correspond to body surface regions whose general structure
is known. When a bi-scale metric is appropriately applied, issues with map
neurons that are not activated by any point in the training data are reduced or
eliminated. The paper also presents results of simulation studies on the
plasticity of bi-scale metric maps when they are retrained af- ter loss of
groups of map neurons or after changes in training data (such as would occur in
a somatotopic map when a body surface region like a finger is lost/removed).
The paper further considers situations where tri-scale met- rics may be useful,
and an alternative approach suggested by neurobiology, where some map regions
adapt more slowly to stimuli because they have a lower learning rate parameter.