pwc-archive/papers-with-abstracts huggingface dataset:
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Authors: Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, Bing Liu
Date: 2017-04-04 00:00:00

Perception and expression of emotion are key factors to the success of
dialogue systems or conversational agents. However, this problem has not been
studied in large-scale conversation generation so far. In this paper, we
propose Emotional Chatting Machine (ECM) that can generate appropriate
responses not only in content (relevant and grammatical) but also in emotion
(emotionally consistent). To the best of our knowledge, this is the first work
that addresses the emotion factor in large-scale conversation generation. ECM
addresses the factor using three new mechanisms that respectively (1) models
the high-level abstraction of emotion expressions by embedding emotion
categories, (2) captures the change of implicit internal emotion states, and
(3) uses explicit emotion expressions with an external emotion vocabulary.
Experiments show that the proposed model can generate responses appropriate not
only in content but also in emotion.
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
Authors: Ke Sun, Mingjie Li, Dong Liu, Jingdong Wang
Date: 2018-06-01 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

In this paper, we are interested in building lightweight and efficient
convolutional neural networks. Inspired by the success of two design patterns,
composition of structured sparse kernels, e.g., interleaved group convolutions
(IGC), and composition of low-rank kernels, e.g., bottle-neck modules, we study
the combination of such two design patterns, using the composition of
structured sparse low-rank kernels, to form a convolutional kernel. Rather than
introducing a complementary condition over channels, we introduce a loose
complementary condition, which is formulated by imposing the complementary
condition over super-channels, to guide the design for generating a dense
convolutional kernel. The resulting network is called IGCV3. We empirically
demonstrate that the combination of low-rank and sparse kernels boosts the
performance and the superiority of our proposed approach to the
state-of-the-arts, IGCV2 and MobileNetV2 over image classification on CIFAR and
ImageNet and object detection on COCO.
Reparameterization Gradient for Non-differentiable Models
Authors: Wonyeol Lee, Hangyeol Yu, Hongseok Yang
Date: 2018-06-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

We present a new algorithm for stochastic variational inference that targets
at models with non-differentiable densities. One of the key challenges in
stochastic variational inference is to come up with a low-variance estimator of
the gradient of a variational objective. We tackle the challenge by
generalizing the reparameterization trick, one of the most effective techniques
for addressing the variance issue for differentiable models, so that the trick
works for non-differentiable models as well. Our algorithm splits the space of
latent variables into regions where the density of the variables is
differentiable, and their boundaries where the density may fail to be
differentiable. For each differentiable region, the algorithm applies the
standard reparameterization trick and estimates the gradient restricted to the
region. For each potentially non-differentiable boundary, it uses a form of
manifold sampling and computes the direction for variational parameters that,
if followed, would increase the boundary's contribution to the variational
objective. The sum of all the estimates becomes the gradient estimate of our
algorithm. Our estimator enjoys the reduced variance of the reparameterization
gradient while remaining unbiased even for non-differentiable models. The
experiments with our preliminary implementation confirm the benefit of reduced
variance and unbiasedness.
On Oracle-Efficient PAC RL with Rich Observations
Authors: Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Date: 2018-03-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study the computational tractability of PAC reinforcement learning with
rich observations. We present new provably sample-efficient algorithms for
environments with deterministic hidden state dynamics and stochastic rich
observations. These methods operate in an oracle model of computation --
accessing policy and value function classes exclusively through standard
optimization primitives -- and therefore represent computationally efficient
alternatives to prior algorithms that require enumeration. With stochastic
hidden state dynamics, we prove that the only known sample-efficient algorithm,
OLIVE, cannot be implemented in the oracle model. We also present several
examples that illustrate fundamental challenges of tractable PAC reinforcement
learning in such general settings.
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Authors: Ramtin Keramati, Jay Whang, Patrick Cho, Emma Brunskill
Date: 2018-06-01 00:00:00
Tasks: Model-based Reinforcement Learning, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans learn to play video games significantly faster than the
state-of-the-art reinforcement learning (RL) algorithms. People seem to build
simple models that are easy to learn to support planning and strategic
exploration. Inspired by this, we investigate two issues in leveraging
model-based RL for sample efficiency. First we investigate how to perform
strategic exploration when exact planning is not feasible and empirically show
that optimistic Monte Carlo Tree Search outperforms posterior sampling methods.
Second we show how to learn simple deterministic models to support fast
learning using object representation. We illustrate the benefit of these ideas
by introducing a novel algorithm, Strategic Object Oriented Reinforcement
Learning (SOORL), that outperforms state-of-the-art algorithms in the game of
Pitfall! in less than 50 episodes.
Community Recovery in a Preferential Attachment Graph
Authors: Bruce Hajek, Suryanarayana Sankagiri
Date: 2018-01-21 00:00:00

A message passing algorithm is derived for recovering communities within a
graph generated by a variation of the Barab\'{a}si-Albert preferential
attachment model. The estimator is assumed to know the arrival times, or order
of attachment, of the vertices. The derivation of the algorithm is based on
belief propagation under an independence assumption. Two precursors to the
message passing algorithm are analyzed: the first is a degree thresholding (DT)
algorithm and the second is an algorithm based on the arrival times of the
children (C) of a given vertex, where the children of a given vertex are the
vertices that attached to it. Comparison of the performance of the algorithms
shows it is beneficial to know the arrival times, not just the number, of the
children. The probability of correct classification of a vertex is
asymptotically determined by the fraction of vertices arriving before it. Two
extensions of Algorithm C are given: the first is based on joint likelihood of
the children of a fixed set of vertices; it can sometimes be used to seed the
message passing algorithm. The second is the message passing algorithm.
Simulation results are given.
Memory Augmented Self-Play
Authors: Shagun Sodhani, Vardaan Pahuja
Date: 2018-05-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Self-play is an unsupervised training procedure which enables the
reinforcement learning agents to explore the environment without requiring any
external rewards. We augment the self-play setting by providing an external
memory where the agent can store experience from the previous tasks. This
enables the agent to come up with more diverse self-play tasks resulting in
faster exploration of the environment. The agent pretrained in the memory
augmented self-play setting easily outperforms the agent pretrained in
no-memory self-play setting.
Training LSTM Networks with Resistive Cross-Point Devices
Authors: Tayfun Gokmen, Malte Rasch, Wilfried Haensch
Date: 2018-06-01 00:00:00

In our previous work we have shown that resistive cross point devices, so
called Resistive Processing Unit (RPU) devices, can provide significant power
and speed benefits when training deep fully connected networks as well as
convolutional neural networks. In this work, we further extend the RPU concept
for training recurrent neural networks (RNNs) namely LSTMs. We show that the
mapping of recurrent layers is very similar to the mapping of fully connected
layers and therefore the RPU concept can potentially provide large acceleration
factors for RNNs as well. In addition, we study the effect of various device
imperfections and system parameters on training performance. Symmetry of
updates becomes even more crucial for RNNs; already a few percent asymmetry
results in an increase in the test error compared to the ideal case trained
with floating point numbers. Furthermore, the input signal resolution to device
arrays needs to be at least 7 bits for successful training. However, we show
that a stochastic rounding scheme can reduce the input signal resolution back
to 5 bits. Further, we find that RPU device variations and hardware noise are
enough to mitigate overfitting, so that there is less need for using dropout.
We note that the models trained here are roughly 1500 times larger than the
fully connected network trained on MNIST dataset in terms of the total number
of multiplication and summation operations performed per epoch. Thus, here we
attempt to study the validity of the RPU approach for large scale networks.
Fitting a deeply-nested hierarchical model to a large book review dataset using a moment-based estimator
Authors: Ningshan Zhang, Kyle Schmaus, Patrick O. Perry
Date: 2018-06-01 00:00:00
Tasks: Recommendation Systems

We consider a particular instance of a common problem in recommender systems:
using a database of book reviews to inform user-targeted recommendations. In
our dataset, books are categorized into genres and sub-genres. To exploit this
nested taxonomy, we use a hierarchical model that enables information pooling
across across similar items at many levels within the genre hierarchy. The main
challenge in deploying this model is computational: the data sizes are large,
and fitting the model at scale using off-the-shelf maximum likelihood
procedures is prohibitive. To get around this computational bottleneck, we
extend a moment-based fitting procedure proposed for fitting single-level
hierarchical models to the general case of arbitrarily deep hierarchies. This
extension is an order of magnetite faster than standard maximum likelihood
procedures. The fitting method can be deployed beyond recommender systems to
general contexts with deeply-nested hierarchical generalized linear mixed
models.
Neural Control Variates for Variance Reduction
Authors: Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu
Date: 2018-06-01 00:00:00
Tasks: Reinforcement Learning

In statistics and machine learning, approximation of an intractable integration is often achieved by using the unbiased Monte Carlo estimator, but the variances of the estimation are generally high in many applications. Control variates approaches are well-known to reduce the variance of the estimation. These control variates are typically constructed by employing predefined parametric functions or polynomials, determined by using those samples drawn from the relevant distributions. Instead, we propose to construct those control variates by learning neural networks to handle the cases when test functions are complex. In many applications, obtaining a large number of samples for Monte Carlo estimation is expensive, which may result in overfitting when training a neural network. We thus further propose to employ auxiliary random variables induced by the original ones to extend data samples for training the neural networks. We apply the proposed control variates with augmented variables to thermodynamic integration and reinforcement learning. Experimental results demonstrate that our method can achieve significant variance reduction compared with other alternatives.
Understanding Convolution for Semantic Segmentation
Authors: Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell
Date: 2017-02-27 00:00:00
Tasks: Segmentation, Semantic Segmentation, Thermal Image Segmentation

Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical value. First, we design dense upsampling convolution
(DUC) to generate pixel-level prediction, which is able to capture and decode
more detailed information that is generally missing in bilinear upsampling.
Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
phase. This framework 1) effectively enlarges the receptive fields (RF) of the
network to aggregate global information; 2) alleviates what we call the
"gridding issue" caused by the standard dilated convolution operation. We
evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
state-of-art result of 80.1% mIOU in the test set at the time of submission. We
also have achieved state-of-the-art overall on the KITTI road estimation
benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
found at https://github.com/TuSimple/TuSimple-DUC .
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Authors: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa
Date: 2017-10-28 00:00:00
Proceeding: WS 2018 7
Tasks: Decoder, Representation Learning, Sentence

Context plays an important role in human language understanding, thus it may
also be useful for machines learning vector representations of language. In
this paper, we explore an asymmetric encoder-decoder structure for unsupervised
context-based sentence representation learning. We carefully designed
experiments to show that neither an autoregressive decoder nor an RNN decoder
is required. After that, we designed a model which still keeps an RNN as the
encoder, while using a non-autoregressive convolutional decoder. We further
combine a suite of effective designs to significantly improve model efficiency
while also achieving better performance. Our model is trained on two different
large unlabelled corpora, and in both cases the transferability is evaluated on
a set of downstream NLP tasks. We empirically show that our model is simple and
fast while producing rich sentence representations that excel in downstream
tasks.
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Authors: Adam S. Charles
Date: 2018-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Learning Theory

Theoretical understanding of deep learning is one of the most important tasks
facing the statistics and machine learning communities. While deep neural
networks (DNNs) originated as engineering methods and models of biological
networks in neuroscience and psychology, they have quickly become a centerpiece
of the machine learning toolbox. Unfortunately, DNN adoption powered by recent
successes combined with the open-source nature of the machine learning
community, has outpaced our theoretical understanding. We cannot reliably
identify when and why DNNs will make mistakes. In some applications like text
translation these mistakes may be comical and provide for fun fodder in
research talks, a single error can be very costly in tasks like medical
imaging. As we utilize DNNs in increasingly sensitive applications, a better
understanding of their properties is thus imperative. Recent advances in DNN
theory are numerous and include many different sources of intuition, such as
learning theory, sparse signal analysis, physics, chemistry, and psychology. An
interesting pattern begins to emerge in the breadth of possible
interpretations. The seemingly limitless approaches are mostly constrained by
the lens with which the mathematical operations are viewed. Ultimately, the
interpretation of DNNs appears to mimic a type of Rorschach test --- a
psychological test wherein subjects interpret a series of seemingly ambiguous
ink-blots. Validation for DNN theory requires a convergence of the literature.
We must distinguish between universal results that are invariant to the
analysis perspective and those that are specific to a particular network
configuration. Simultaneously we must deal with the fact that many standard
statistical tools for quantifying generalization or empirically assessing
important network features are difficult to apply to DNNs.
Tandem Blocks in Deep Convolutional Neural Networks
Authors: Chris Hettinger, Tanner Christensen, Jeffrey Humpherys, Tyler J. Jarvis
Date: 2018-06-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, image-classification, Image Classification

Due to the success of residual networks (resnets) and related architectures,
shortcut connections have quickly become standard tools for building
convolutional neural networks. The explanations in the literature for the
apparent effectiveness of shortcuts are varied and often contradictory. We
hypothesize that shortcuts work primarily because they act as linear
counterparts to nonlinear layers. We test this hypothesis by using several
variations on the standard residual block, with different types of linear
connections, to build small image classification networks. Our experiments show
that other kinds of linear connections can be even more effective than the
identity shortcuts. Our results also suggest that the best type of linear
connection for a given application may depend on both network width and depth.
Invariance of Weight Distributions in Rectified MLPs
Authors: Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher
Date: 2017-11-24 00:00:00
Proceeding: ICML 2018 7

An interesting approach to analyzing neural networks that has received
renewed attention is to examine the equivalent kernel of the neural network.
This is based on the fact that a fully connected feedforward network with one
hidden layer, a certain weight distribution, an activation function, and an
infinite number of neurons can be viewed as a mapping into a Hilbert space. We
derive the equivalent kernels of MLPs with ReLU or Leaky ReLU activations for
all rotationally-invariant weight distributions, generalizing a previous result
that required Gaussian weight distributions. Additionally, the Central Limit
Theorem is used to show that for certain activation functions, kernels
corresponding to layers with weight distributions having $0$ mean and finite
absolute third moment are asymptotically universal, and are well approximated
by the kernel corresponding to layers with spherical Gaussian weights. In deep
networks, as depth increases the equivalent kernel approaches a pathological
fixed point, which can be used to argue why training randomly initialized
networks can be difficult. Our results also have implications for weight
initialization.
Sea surface temperature prediction and reconstruction using patch-level neural network representations
Authors: Said Ouala, Cedric Herzet, Ronan Fablet
Date: 2018-06-01 00:00:00
Tasks: Numerical Integration, Time Series, Time Series Analysis

The forecasting and reconstruction of ocean and atmosphere dynamics from
satellite observation time series are key challenges. While model-driven
representations remain the classic approaches, data-driven representations
become more and more appealing to benefit from available large-scale
observation and simulation datasets. In this work we investigate the relevance
of recently introduced bilinear residual neural network representations, which
mimic numerical integration schemes such as Runge-Kutta, for the forecasting
and assimilation of geophysical fields from satellite-derived remote sensing
data. As a case-study, we consider satellite-derived Sea Surface Temperature
time series off South Africa, which involves intense and complex upper ocean
dynamics. Our numerical experiments demonstrate that the proposed patch-level
neural-network-based representations outperform other data-driven models,
including analog schemes, both in terms of forecasting and missing data
interpolation performance with a relative gain up to 50\% for highly dynamic
areas.
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
Authors: Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
Date: 2018-06-01 00:00:00
Tasks: Decision Making

This paper presents a learning from demonstration approach to programming
safe, autonomous behaviors for uncommon driving scenarios. Simulation is used
to re-create a targeted driving situation, one containing a road-side hazard
creating a significant occlusion in an urban neighborhood, and collect optimal
driving behaviors from 24 users. Paper employs a key-frame based approach
combined with an algorithm to linearly combine models in order to extend the
behavior to novel variations of the target situation. This approach is
theoretically agnostic to the kind of LfD framework used for modeling data and
our results suggest it generalizes well to variations containing an additional
number of hazards occurring in sequence. The linear combination algorithm is
informed by analysis of driving data, which also suggests that decision-making
algorithms need to consider a trade-off between road-rules and immediate
rewards to tackle some complex cases.
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
Authors: Xiaoyu Li, Francesco Orabona
Date: 2018-05-21 00:00:00

Stochastic gradient descent is the method of choice for large scale
optimization of machine learning objective functions. Yet, its performance is
greatly variable and heavily depends on the choice of the stepsizes. This has
motivated a large body of research on adaptive stepsizes. However, there is
currently a gap in our theoretical understanding of these methods, especially
in the non-convex setting. In this paper, we start closing this gap: we
theoretically analyze in the convex and non-convex settings a generalized
version of the AdaGrad stepsizes. We show sufficient conditions for these
stepsizes to achieve almost sure asymptotic convergence of the gradients to
zero, proving the first guarantee for generalized AdaGrad stepsizes in the
non-convex setting. Moreover, we show that these stepsizes allow to
automatically adapt to the level of noise of the stochastic gradients in both
the convex and non-convex settings, interpolating between $O(1/T)$ and
$O(1/\sqrt{T})$, up to logarithmic terms.