pwc-archive/papers-with-abstracts huggingface dataset:
Stack-Captioning: Coarse-to-Fine Learning for Image Captioning
Authors: Jiuxiang Gu, Jianfei Cai, Gang Wang, Tsuhan Chen
Date: 2017-09-11 00:00:00
Tasks: Decoder, Image Captioning, Reinforcement Learning, Sentence

The existing image captioning approaches typically train a one-stage sentence
decoder, which is difficult to generate rich fine-grained descriptions. On the
other hand, multi-stage image caption model is hard to train due to the
vanishing gradient problem. In this paper, we propose a coarse-to-fine
multi-stage prediction framework for image captioning, composed of multiple
decoders each of which operates on the output of the previous stage, producing
increasingly refined image descriptions. Our proposed learning approach
addresses the difficulty of vanishing gradients during training by providing a
learning objective function that enforces intermediate supervisions.
Particularly, we optimize our model with a reinforcement learning approach
which utilizes the output of each intermediate decoder's test-time inference
algorithm as well as the output of its preceding decoder to normalize the
rewards, which simultaneously solves the well-known exposure bias problem and
the loss-evaluation mismatch problem. We extensively evaluate the proposed
approach on MSCOCO and show that our approach can achieve the state-of-the-art
performance.
Neural Network Renormalization Group
Authors: Shuo-Hui Li, Lei Wang
Date: 2018-02-08 00:00:00

We present a variational renormalization group (RG) approach using a deep
generative model based on normalizing flows. The model performs hierarchical
change-of-variables transformations from the physical space to a latent space
with reduced mutual information. Conversely, the neural net directly maps
independent Gaussian noises to physical configurations following the inverse RG
flow. The model has an exact and tractable likelihood, which allows unbiased
training and direct access to the renormalized energy function of the latent
variables. To train the model, we employ probability density distillation for
the bare energy function of the physical problem, in which the training loss
provides a variational upper bound of the physical free energy. We demonstrate
practical usage of the approach by identifying mutually independent collective
variables of the Ising model and performing accelerated hybrid Monte Carlo
sampling in the latent space. Lastly, we comment on the connection of the
present approach to the wavelet formulation of RG and the modern pursuit of
information preserving RG.
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Authors: Hongqiao Wang, Jinglai Li
Date: 2017-03-29 00:00:00
Tasks: Active Learning, Bayesian Inference

We consider Bayesian inference problems with computationally intensive
likelihood functions. We propose a Gaussian process (GP) based method to
approximate the joint distribution of the unknown parameters and the data. In
particular, we write the joint density approximately as a product of an
approximate posterior density and an exponentiated GP surrogate. We then
provide an adaptive algorithm to construct such an approximation, where an
active learning method is used to choose the design points. With numerical
examples, we illustrate that the proposed method has competitive performance
against existing approaches for Bayesian computation.
Topology Reduction in Deep Convolutional Feature Extraction Networks
Authors: Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
Date: 2017-07-10 00:00:00

Deep convolutional neural networks (CNNs) used in practice employ potentially
hundreds of layers and $10$,$000$s of nodes. Such network sizes entail
significant computational complexity due to the large number of convolutions
that need to be carried out; in addition, a large number of parameters needs to
be learned and stored. Very deep and wide CNNs may therefore not be well suited
to applications operating under severe resource constraints as is the case,
e.g., in low-power embedded and mobile platforms. This paper aims at
understanding the impact of CNN topology, specifically depth and width, on the
network's feature extraction capabilities. We address this question for the
class of scattering networks that employ either Weyl-Heisenberg filters or
wavelets, the modulus non-linearity, and no pooling. The exponential feature
map energy decay results in Wiatowski et al., 2017, are generalized to
$\mathcal{O}(a^{-N})$, where an arbitrary decay factor $a>1$ can be realized
through suitable choice of the Weyl-Heisenberg prototype function or the mother
wavelet. We then show how networks of fixed (possibly small) depth $N$ can be
designed to guarantee that $((1-\varepsilon)\cdot 100)\%$ of the input signal's
energy are contained in the feature vector. Based on the notion of
operationally significant nodes, we characterize, partly rigorously and partly
heuristically, the topology-reducing effects of (effectively) band-limited
input signals, band-limited filters, and feature map symmetries. Finally, for
networks based on Weyl-Heisenberg filters, we determine the prototype function
bandwidth that minimizes---for fixed network depth $N$---the average number of
operationally significant nodes per layer.
Where to put the Image in an Image Caption Generator
Authors: Marc Tanti, Albert Gatt, Kenneth P. Camilleri
Date: 2017-03-27 00:00:00
Tasks: Caption Generation, Language Modeling, Language Modelling

When a recurrent neural network language model is used for caption
generation, the image information can be fed to the neural network either by
directly incorporating it in the RNN -- conditioning the language model by
`injecting' image features -- or in a layer following the RNN -- conditioning
the language model by `merging' image features. While both options are attested
in the literature, there is as yet no systematic comparison between the two. In
this paper we empirically show that it is not especially detrimental to
performance whether one architecture is used or another. The merge architecture
does have practical advantages, as conditioning by merging allows the RNN's
hidden state vector to shrink in size by up to four times. Our results suggest
that the visual and linguistic modalities for caption generation need not be
jointly encoded by the RNN as that yields large, memory-intensive models with
few tangible advantages in performance; rather, the multimodal integration
should be delayed to a subsequent stage.
A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces
Authors: Jan Zacharias, Michael Barz, Daniel Sonntag
Date: 2018-03-13 00:00:00
Tasks: Deep Learning

This paper provides an overview of prominent deep learning toolkits and, in
particular, reports on recent publications that contributed open source
software for implementing tasks that are common in intelligent user interfaces
(IUI). We provide a scientific reference for researchers and software engineers
who plan to utilise deep learning techniques within their IUI research and
development projects.
How to evaluate sentiment classifiers for Twitter time-ordered data?
Authors: Igor Mozetič, Luis Torgo, Vitor Cerqueira, Jasmina Smailović
Date: 2018-03-14 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

Social media are becoming an increasingly important source of information
about the public mood regarding issues such as elections, Brexit, stock market,
etc. In this paper we focus on sentiment classification of Twitter data.
Construction of sentiment classifiers is a standard text mining task, but here
we address the question of how to properly evaluate them as there is no settled
way to do so. Sentiment classes are ordered and unbalanced, and Twitter
produces a stream of time-ordered data. The problem we address concerns the
procedures used to obtain reliable estimates of performance measures, and
whether the temporal ordering of the training and test data matters. We
collected a large set of 1.5 million tweets in 13 European languages. We
created 138 sentiment models and out-of-sample datasets, which are used as a
gold standard for evaluations. The corresponding 138 in-sample datasets are
used to empirically compare six different estimation procedures: three variants
of cross-validation, and three variants of sequential validation (where test
set always follows the training set). We find no significant difference between
the best cross-validation and sequential validation. However, we observe that
all cross-validation variants tend to overestimate the performance, while the
sequential methods tend to underestimate it. Standard cross-validation with
random selection of examples is significantly worse than the blocked
cross-validation, and should not be used to evaluate classifiers in
time-ordered data scenarios.
Efficient Training for Positive Unlabeled Learning
Authors: Emanuele Sansone, Francesco G. B. De Natale, Zhi-Hua Zhou
Date: 2016-08-24 00:00:00
Tasks: Learning Theory

Positive unlabeled (PU) learning is useful in various practical situations,
where there is a need to learn a classifier for a class of interest from an
unlabeled data set, which may contain anomalies as well as samples from unknown
classes. The learning task can be formulated as an optimization problem under
the framework of statistical learning theory. Recent studies have theoretically
analyzed its properties and generalization performance, nevertheless, little
effort has been made to consider the problem of scalability, especially when
large sets of unlabeled data are available. In this work we propose a novel
scalable PU learning algorithm that is theoretically proven to provide the
optimal solution, while showing superior computational and memory performance.
Experimental evaluation confirms the theoretical evidence and shows that the
proposed method can be successfully applied to a large variety of real-world
problems involving PU learning.
The 2017 AIBIRDS Competition
Authors: Matthew Stephenson, Jochen Renz, Xiaoyu Ge, Peng Zhang
Date: 2018-03-14 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

This paper presents an overview of the sixth AIBIRDS competition, held at the
26th International Joint Conference on Artificial Intelligence. This
competition tasked participants with developing an intelligent agent which can
play the physics-based puzzle game Angry Birds. This game uses a sophisticated
physics engine that requires agents to reason and predict the outcome of
actions with only limited environmental information. Agents entered into this
competition were required to solve a wide assortment of previously unseen
levels within a set time limit. The physical reasoning and planning required to
solve these levels are very similar to those of many real-world problems. This
year's competition featured some of the best agents developed so far and even
included several new AI techniques such as deep reinforcement learning. Within
this paper we describe the framework, rules, submitted agents and results for
this competition. We also provide some background information on related work
and other video game AI competitions, as well as discussing some potential
ideas for future AIBIRDS competitions and agent improvements.
A Study of Car-to-Train Assignment Problem for Rail Express Cargos on Scheduled and Unscheduled Train Service Network
Authors: Boliang Lin
Date: 2018-03-14 00:00:00

Freight train services in a railway network system are generally divided into
two categories: one is the unscheduled train, whose operating frequency
fluctuates with origin-destination (OD) demands; the other is the scheduled
train, which is running based on regular timetable just like the passenger
trains. The timetable will be released to the public if determined and it would
not be influenced by OD demands. Typically, the total capacity of scheduled
trains can usually satisfy the predicted demands of express cargos in average.
However, the demands are changing in practice. Therefore, how to distribute the
shipments between different stations to unscheduled and scheduled train
services has become an important research field in railway transportation. This
paper focuses on the coordinated optimization of the rail express cargos
distribution in two service networks. On the premise of fully utilizing the
capacity of scheduled service network first, we established a Car-to-Train
(CTT) assignment model to assign rail express cargos to scheduled and
unscheduled trains scientifically. The objective function is to maximize the
net income of transporting the rail express cargos. The constraints include the
capacity restriction on the service arcs, flow balance constraints, logical
relationship constraint between two groups of decision variables and the due
date constraint. The last constraint is to ensure that the total transportation
time of a shipment would not be longer than its predefined due date. Finally,
we discuss the linearization techniques to simplify the model proposed in this
paper, which make it possible for obtaining global optimal solution by using
the commercial software.
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Authors: Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey
Date: 2018-01-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Defense

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against
adversarial examples, which are carefully crafted instances that can mislead
DNNs to make errors during prediction. To better understand such attacks, a
characterization is needed of the properties of regions (the so-called
'adversarial subspaces') in which adversarial examples lie. We tackle this
challenge by characterizing the dimensional properties of adversarial regions,
via the use of Local Intrinsic Dimensionality (LID). LID assesses the
space-filling capability of the region surrounding a reference example, based
on the distance distribution of the example to its neighbors. We first provide
explanations about how adversarial perturbation can affect the LID
characteristic of adversarial regions, and then show empirically that LID
characteristics can facilitate the distinction of adversarial examples
generated using state-of-the-art attacks. As a proof-of-concept, we show that a
potential application of LID is to distinguish adversarial examples, and the
preliminary results show that it can outperform several state-of-the-art
detection measures by large margins for five attack strategies considered in
this paper across three benchmark datasets. Our analysis of the LID
characteristic for adversarial regions not only motivates new directions of
effective adversarial defense, but also opens up more challenges for developing
new attacks to better understand the vulnerabilities of DNNs.
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Authors: Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, Lei Zhang
Date: 2017-07-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Captioning, Visual Question Answering, Visual Question Answering (VQA)

Top-down visual attention mechanisms have been used extensively in image
captioning and visual question answering (VQA) to enable deeper image
understanding through fine-grained analysis and even multiple steps of
reasoning. In this work, we propose a combined bottom-up and top-down attention
mechanism that enables attention to be calculated at the level of objects and
other salient image regions. This is the natural basis for attention to be
considered. Within our approach, the bottom-up mechanism (based on Faster
R-CNN) proposes image regions, each with an associated feature vector, while
the top-down mechanism determines feature weightings. Applying this approach to
image captioning, our results on the MSCOCO test server establish a new
state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of
117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of
the method, applying the same approach to VQA we obtain first place in the 2017
VQA Challenge.
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
Authors: Arghya Pal, Vineeth N. Balasubramanian
Date: 2018-03-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Multi-Task Learning, Transfer Learning

Paucity of large curated hand-labeled training data for every
domain-of-interest forms a major bottleneck in the deployment of machine
learning models in computer vision and other fields. Recent work (Data
Programming) has shown how distant supervision signals in the form of labeling
functions can be used to obtain labels for given data in near-constant time. In
this work, we present Adversarial Data Programming (ADP), which presents an
adversarial methodology to generate data as well as a curated aggregated label
has given a set of weak labeling functions. We validated our method on the
MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many
state-of-the-art models. We conducted extensive experiments to study its
usefulness, as well as showed how the proposed ADP framework can be used for
transfer learning as well as multi-task learning, where data from two domains
are generated simultaneously using the framework along with the label
information. Our future work will involve understanding the theoretical
implications of this new framework from a game-theoretic perspective, as well
as explore the performance of the method on more complex datasets.
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
Authors: Yazhen Wang
Date: 2017-11-27 00:00:00
Tasks: Stochastic Optimization

This paper investigates asymptotic behaviors of gradient descent algorithms (particularly accelerated gradient descent and stochastic gradient descent) in the context of stochastic optimization arising in statistics and machine learning where objective functions are estimated from available data. We show that these algorithms can be computationally modeled by continuous-time ordinary or stochastic differential equations. We establish gradient flow central limit theorems to describe the limiting dynamic behaviors of these computational algorithms and the large-sample performances of the related statistical procedures, as the number of algorithm iterations and data size both go to infinity, where the gradient flow central limit theorems are governed by some linear ordinary or stochastic differential equations like time-dependent Ornstein-Uhlenbeck processes. We illustrate that our study can provide a novel unified framework for a joint computational and statistical asymptotic analysis, where the computational asymptotic analysis studies dynamic behaviors of these algorithms with the time (or the number of iterations in the algorithms), the statistical asymptotic analysis investigates large sample behaviors of the statistical procedures (like estimators and classifiers) that the algorithms are applied to compute, and in fact the statistical procedures are equal to the limits of the random sequences generated from these iterative algorithms as the number of iterations goes to infinity. The joint analysis results based on the obtained gradient flow central limit theorems can identify four factors - learning rate, batch size, gradient covariance, and Hessian - to derive new theory regarding the local minima found by stochastic gradient descent for solving non-convex optimization problems.
Neuron inspired data encoding memristive multi-level memory cell
Authors: Aidana Irmanova, Alex Pappachen James
Date: 2018-03-14 00:00:00

Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware
require shifting the signal processing from digital to the analog domain,
demanding memory technologies beyond conventional CMOS binary storage units.
Using memristors for building analog data storage is one of the promising
approaches amongst emerging non-volatile memory technologies. Recently, a
memristive multi-level memory (MLM) cell for storing discrete analog values has
been developed in which memory system is implemented combining memristors in
voltage divider configuration. In given example, the memory cell of 3 sub-cells
with a memristor in each was programmed to store ternary bits which overall
achieved 10 and 27 discrete voltage levels. However, for further use of
proposed memory cell in analog signal processing circuits data encoder is
required to generate control voltages for programming memristors to store
discrete analog values. In this paper, we present the design and performance
analysis of data encoder that generates write pattern signals for 10 level
memristive memory.
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Authors: Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang
Date: 2018-03-14 00:00:00

Recently, significant accuracy improvement has been achieved for acoustic
recognition systems by increasing the model size of Long Short-Term Memory
(LSTM) networks. Unfortunately, the ever-increasing size of LSTM model leads to
inefficient designs on FPGAs due to the limited on-chip resources. The previous
work proposes to use a pruning based compression technique to reduce the model
size and thus speedups the inference on FPGAs. However, the random nature of
the pruning technique transforms the dense matrices of the model to highly
unstructured sparse ones, which leads to unbalanced computation and irregular
memory accesses and thus hurts the overall performance and energy efficiency.
  In contrast, we propose to use a structured compression technique which could
not only reduce the LSTM model size but also eliminate the irregularities of
computation and memory accesses. This approach employs block-circulant instead
of sparse matrices to compress weight matrices and reduces the storage
requirement from $\mathcal{O}(k^2)$ to $\mathcal{O}(k)$. Fast Fourier Transform
algorithm is utilized to further accelerate the inference by reducing the
computational complexity from $\mathcal{O}(k^2)$ to
$\mathcal{O}(k\text{log}k)$. The datapath and activation functions are
quantized as 16-bit to improve the resource utilization. More importantly, we
propose a comprehensive framework called C-LSTM to automatically optimize and
implement a wide range of LSTM variants on FPGAs. According to the experimental
results, C-LSTM achieves up to 18.8X and 33.5X gains for performance and energy
efficiency compared with the state-of-the-art LSTM implementation under the
same experimental setup, and the accuracy degradation is very small.
Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory
Authors: Olga Krestinskaya, Alex Pappachen James
Date: 2018-03-14 00:00:00
Tasks: Face Recognition

Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates
sparsity, hierarchy and modularity resembling the working principles of
neocortex. Feature encoding is an important step to create sparse binary
patterns. This sparsity is introduced by the binary weights and random weight
assignment in the initialization stage of the HTM. We propose the alternative
deterministic method for the HTM initialization stage, which connects the HTM
weights to the input data and preserves natural sparsity of the input
information. Further, we introduce the hardware implementation of the
deterministic approach and compare it to the traditional HTM and existing
hardware implementation. We test the proposed approach on the face recognition
problem and show that it outperforms the conventional HTM approach.