pwc-archive/papers-with-abstracts huggingface dataset:
Learning the Compositional Spaces for Generalized Zero-shot Learning
Authors: Hanze Dong, Yanwei Fu, Sung Ju Hwang, Leonid Sigal, xiangyang xue
Date: 2018-10-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generalized Zero-Shot Learning, Open Set Learning, Zero-Shot Learning

This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time. We propose a novel space decomposition method to solve G-ZSL. Some previous models with space decomposition operations only calibrate the confident prediction of source classes (W-SVM [46]) or take target-class instances as outliers [49]. In contrast, we propose to directly estimate and fine-tune the decision boundary between the source and the target classes. Specifically, we put forward a framework that enables to learn compositional spaces by splitting the instances into Source, Target, and Uncertain spaces and perform recognition in each space, where the uncertain space contains instances whose labels cannot be confidently predicted. We use two statistical tools, namely, bootstrapping and Kolmogorov-Smirnov (K-S) Test, to learn the compositional spaces for G-ZSL. We validate our method extensively on multiple G-ZSL benchmarks, on which it achieves state-of-the-art performances.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Authors: Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu
Date: 2018-10-17 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Reading Comprehension, Reading Comprehension, Sentence

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018
Learning in Non-convex Games with an Optimization Oracle
Authors: Naman Agarwal, Alon Gonen, Elad Hazan
Date: 2018-10-17 00:00:00

We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially more computation than statistical learning. In this paper we show that by slightly strengthening the oracle model, the online and the statistical learning models become computationally equivalent. Our result holds for any Lipschitz and bounded (but not necessarily convex) function. As an application we demonstrate how the offline oracle enables efficient computation of an equilibrium in non-convex games, that include GAN (generative adversarial networks) as a special case.
Fault Tolerance in Iterative-Convergent Machine Learning
Authors: Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing
Date: 2018-10-17 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) training algorithms often possess an inherent
self-correcting behavior due to their iterative-convergent nature. Recent
systems exploit this property to achieve adaptability and efficiency in
unreliable computing environments by relaxing the consistency of execution and
allowing calculation errors to be self-corrected during training. However, the
behavior of such systems are only well understood for specific types of
calculation errors, such as those caused by staleness, reduced precision, or
asynchronicity, and for specific types of training algorithms, such as
stochastic gradient descent. In this paper, we develop a general framework to
quantify the effects of calculation errors on iterative-convergent algorithms
and use this framework to design new strategies for checkpoint-based fault
tolerance. Our framework yields a worst-case upper bound on the iteration cost
of arbitrary perturbations to model parameters during training. Our system,
SCAR, employs strategies which reduce the iteration cost upper bound due to
perturbations incurred when recovering from checkpoints. We show that SCAR can
reduce the iteration cost of partial failures by 78% - 95% when compared with
traditional checkpoint-based fault tolerance across a variety of ML models and
training algorithms.
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
Authors: Andri Ashfahani, Mahardhika Pratama
Date: 2018-10-17 00:00:00
Tasks: Continual Learning, Deep Learning, Drift Detection

The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. The maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures.
Exploring Sentence Vector Spaces through Automatic Summarization
Authors: Adly Templeton, Jugal Kalita
Date: 2018-10-16 00:00:00
Tasks: Sentence, Sentence Embeddings

Given vector representations for individual words, it is necessary to compute
vector representations of sentences for many applications in a compositional
manner, often using artificial neural networks.
  Relatively little work has explored the internal structure and properties of
such sentence vectors. In this paper, we explore the properties of sentence
vectors in the context of automatic summarization. In particular, we show that
cosine similarity between sentence vectors and document vectors is strongly
correlated with sentence importance and that vector semantics can identify and
correct gaps between the sentences chosen so far and the document. In addition,
we identify specific dimensions which are linked to effective summaries. To our
knowledge, this is the first time specific dimensions of sentence embeddings
have been connected to sentence properties. We also compare the features of
different methods of sentence embeddings. Many of these insights have
applications in uses of sentence embeddings far beyond summarization.
Finding Options that Minimize Planning Time
Authors: Yuu Jinnai, David Abel, D. Ellis Hershkowitz, Michael Littman, George Konidaris
Date: 2018-10-16 00:00:00

We formalize the problem of selecting the optimal set of options for planning
as that of computing the smallest set of options so that planning converges in
less than a given maximum of value-iteration passes. We first show that the
problem is NP-hard, even if the task is constrained to be deterministic---the
first such complexity result for option discovery. We then present the first
polynomial-time boundedly suboptimal approximation algorithm for this setting,
and empirically evaluate it against both the optimal options and a
representative collection of heuristic approaches in simple grid-based domains
including the classic four-rooms problem.
Prediction of Atomization Energy Using Graph Kernel and Active Learning
Authors: Yu-Hang Tang, Wibe A. de Jong
Date: 2018-10-16 00:00:00
Tasks: Active Learning, Management

Data-driven prediction of molecular properties presents unique challenges to
the design of machine learning methods concerning data
structure/dimensionality, symmetry adaption, and confidence management. In this
paper, we present a kernel-based pipeline that can learn and predict the
atomization energy of molecules with high accuracy. The framework employs
Gaussian process regression to perform predictions based on the similarity
between molecules, which is computed using the marginalized graph kernel. To
apply the marginalized graph kernel, a spatial adjacency rule is first employed
to convert molecules into graphs whose vertices and edges are labeled by
elements and interatomic distances, respectively. We then derive formulas for
the efficient evaluation of the kernel. Specific functional components for the
marginalized graph kernel are proposed, while the effect of the associated
hyperparameters on accuracy and predictive confidence are examined. We show
that the graph kernel is particularly suitable for predicting extensive
properties because its convolutional structure coincides with that of the
covariance formula between sums of random variables. Using an active learning
procedure, we demonstrate that the proposed method can achieve a mean absolute
error of 0.62 +- 0.01 kcal/mol using as few as 2000 training samples on the QM7
data set.
Deep neural network based i-vector mapping for speaker verification using short utterances
Authors: Jinxi Guo, Ning Xu, Kailun Qian, Yang Shi, Kaiyuan Xu, Ying-Nian Wu, Abeer Alwan
Date: 2018-10-16 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Text-Independent Speaker Recognition

Text-independent speaker recognition using short utterances is a highly
challenging task due to the large variation and content mismatch between short
utterances. I-vector based systems have become the standard in speaker
verification applications, but they are less effective with short utterances.
In this paper, we first compare two state-of-the-art universal background model
training methods for i-vector modeling using full-length and short utterance
evaluation tasks. The two methods are Gaussian mixture model (GMM) based and
deep neural network (DNN) based methods. The results indicate that the
I-vector_DNN system outperforms the I-vector_GMM system under various
durations. However, the performances of both systems degrade significantly as
the duration of the utterances decreases. To address this issue, we propose two
novel nonlinear mapping methods which train DNN models to map the i-vectors
extracted from short utterances to their corresponding long-utterance
i-vectors. The mapped i-vector can restore missing information and reduce the
variance of the original short-utterance i-vectors. The proposed methods both
model the joint representation of short and long utterance i-vectors by using
autoencoder. Experimental results using the NIST SRE 2010 dataset show that
both methods provide significant improvement and result in a max of 28.43%
relative improvement in Equal Error Rates from a baseline system, when using
deep encoder with residual blocks and adding an additional phoneme vector. When
further testing the best-validated models of SRE10 on the Speaker In The Wild
dataset, the methods result in a 23.12% improvement on arbitrary-duration (1-5
s) short-utterance conditions.
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Authors: Mahdi Ahmadi, Alireza Norouzi, S. M. Reza Soroushmehr, Nader Karimi, Kayvan Najarian, Shadrokh Samavi, Ali Emami
Date: 2018-10-16 00:00:00

Due to the rapid growth of machine learning tools and specifically deep
networks in various computer vision and image processing areas, application of
Convolutional Neural Networks for watermarking have recently emerged. In this
paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark)
which can be adapted for any desired transform space. The framework is composed
of two Fully Convolutional Neural Networks with the residual structure for
embedding and extraction. The whole deep network is trained end-to-end to
conduct a blind secure watermarking. The framework is customizable for the
level of robustness vs. imperceptibility. It is also adjustable for the
trade-off between capacity and robustness. The proposed framework simulates
various attacks as a differentiable network layer to facilitate end-to-end
training. For JPEG attack, a differentiable approximation is utilized, which
drastically improves the watermarking robustness to this attack. Another
important characteristic of the proposed framework, which leads to improved
security and robustness, is its capability to diffuse watermark information
among a relatively wide area of the image. Comparative results versus recent
state-of-the-art researches highlight the superiority of the proposed framework
in terms of imperceptibility and robustness.
Solving Tree Problems with Category Theory
Authors: Rafik Hadfi
Date: 2018-10-16 00:00:00

Artificial Intelligence (AI) has long pursued models, theories, and
techniques to imbue machines with human-like general intelligence. Yet even the
currently predominant data-driven approaches in AI seem to be lacking humans'
unique ability to solve wide ranges of problems. This situation begs the
question of the existence of principles that underlie general problem-solving
capabilities. We approach this question through the mathematical formulation of
analogies across different problems and solutions. We focus in particular on
problems that could be represented as tree-like structures. Most importantly,
we adopt a category-theoretic approach in formalising tree problems as
categories, and in proving the existence of equivalences across apparently
unrelated problem domains. We prove the existence of a functor between the
category of tree problems and the category of solutions. We also provide a
weaker version of the functor by quantifying equivalences of problem categories
using a metric on tree problems.
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms
Authors: Vikas K. Garg, Tamar Pichkhadze
Date: 2018-10-16 00:00:00
Proceeding: NeurIPS 2019 12

We resolve the fundamental problem of online decoding with general $n^{th}$ order ergodic Markov chain models. Specifically, we provide deterministic and randomized algorithms whose performance is close to that of the optimal offline algorithm even when latency is small. Our algorithms admit efficient implementation via dynamic programs, and readily extend to (adversarial) non-stationary or time-varying settings. We also establish lower bounds for online methods under latency constraints in both deterministic and randomized settings, and show that no online algorithm can perform significantly better than our algorithms. Empirically, just with latency one, our algorithm outperforms the online step algorithm by over 30\% in terms of decoding agreement with the optimal algorithm on genome sequence data.
Deep Neural Maps
Authors: Mehran Pesteie, Purang Abolmaesumi, Robert Rohling
Date: 2018-10-16 00:00:00
Tasks: Representation Learning

We introduce a new unsupervised representation learning and visualization
using deep convolutional networks and self organizing maps called Deep Neural
Maps (DNM). DNM jointly learns an embedding of the input data and a mapping
from the embedding space to a two-dimensional lattice. We compare
visualizations of DNM with those of t-SNE and LLE on the MNIST and COIL-20 data
sets. Our experiments show that the DNM can learn efficient representations of
the input data, which reflects characteristics of each class. This is shown via
back-projecting the neurons of the map on the data space.
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron
Authors: Sharan Vaswani, Francis Bach, Mark Schmidt
Date: 2018-10-16 00:00:00

Modern machine learning focuses on highly expressive models that are able to
fit or interpolate the data completely, resulting in zero training loss. For
such models, we show that the stochastic gradients of common loss functions
satisfy a strong growth condition. Under this condition, we prove that constant
step-size stochastic gradient descent (SGD) with Nesterov acceleration matches
the convergence rate of the deterministic accelerated method for both convex
and strongly-convex functions. We also show that this condition implies that
SGD can find a first-order stationary point as efficiently as full gradient
descent in non-convex settings. Under interpolation, we further show that all
smooth loss functions with a finite-sum structure satisfy a weaker growth
condition. Given this weaker condition, we prove that SGD with a constant
step-size attains the deterministic convergence rate in both the
strongly-convex and convex settings. Under additional assumptions, the above
results enable us to prove an O(1/k^2) mistake bound for k iterations of a
stochastic perceptron algorithm using the squared-hinge loss. Finally, we
validate our theoretical findings with experiments on synthetic and real
datasets.
Signed iterative random forests to identify enhancer-associated transcription factor binding
Authors: Karl Kumbier, Sumanta Basu, Erwin Frise, Susan E. Celniker, James B. Brown, Susan Celniker, Bin Yu
Date: 2018-10-16 00:00:00
Tasks: Interpretable Machine Learning

Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable machine learning method: signed iterative random forests (siRF), which we use to infer regulatory interactions among transcription factors and functional binding signatures surrounding enhancer elements in Drosophila melanogaster.
At Human Speed: Deep Reinforcement Learning with Action Delay
Authors: Vlad Firoiu, Tina Ju, Josh Tenenbaum
Date: 2018-10-16 00:00:00
Tasks: Board Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of tasks, from video games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning and reinforcement learning, that learn to play from experience
with minimal prior knowledge. However, these machines often do not win through
intelligence alone -- they possess vastly superior speed and precision,
allowing them to act in ways a human never could. To level the playing field,
we restrict the machine's reaction time to a human level, and find that
standard deep reinforcement learning methods quickly drop in performance. We
propose a solution to the action delay problem inspired by human perception --
to endow agents with a neural predictive model of the environment which
"undoes" the delay inherent in their environment -- and demonstrate its
efficacy against professional players in Super Smash Bros. Melee, a popular
console fighting game.
Optimal locally private estimation under $\ell_p$ loss for $1\le p\le 2$
Authors: Min Ye, Alexander Barg
Date: 2018-10-16 00:00:00
Tasks: valid

We consider the minimax estimation problem of a discrete distribution with
support size $k$ under locally differential privacy constraints. A
privatization scheme is applied to each raw sample independently, and we need
to estimate the distribution of the raw samples from the privatized samples. A
positive number $\epsilon$ measures the privacy level of a privatization
scheme.
  In our previous work (IEEE Trans. Inform. Theory, 2018), we proposed a family
of new privatization schemes and the corresponding estimator. We also proved
that our scheme and estimator are order optimal in the regime $e^{\epsilon} \ll
k$ under both $\ell_2^2$ (mean square) and $\ell_1$ loss. In this paper, we
sharpen this result by showing asymptotic optimality of the proposed scheme
under the $\ell_p^p$ loss for all $1\le p\le 2.$ More precisely, we show that
for any $p\in[1,2]$ and any $k$ and $\epsilon,$ the ratio between the
worst-case $\ell_p^p$ estimation loss of our scheme and the optimal value
approaches $1$ as the number of samples tends to infinity. The lower bound on
the minimax risk of private estimation that we establish as a part of the proof
is valid for any loss function $\ell_p^p, p\ge 1.$
The Concept of Criticality in Reinforcement Learning
Authors: Yitzhak Spielberg, Amos Azaria
Date: 2018-10-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning methods carry a well known bias-variance trade-off in
n-step algorithms for optimal control. Unfortunately, this has rarely been
addressed in current research. This trade-off principle holds independent of
the choice of the algorithm, such as n-step SARSA, n-step Expected SARSA or
n-step Tree backup. A small n results in a large bias, while a large n leads to
large variance. The literature offers no straightforward recipe for the best
choice of this value. While currently all n-step algorithms use a fixed value
of n over the state space we extend the framework of n-step updates by allowing
each state to have its specific n.
  We propose a solution to this problem within the context of human aided
reinforcement learning. Our approach is based on the observation that a human
can learn more efficiently if she receives input regarding the criticality of a
given state and thus the amount of attention she needs to invest into the
learning in that state. This observation is related to the idea that each state
of the MDP has a certain measure of criticality which indicates how much the
choice of the action in that state influences the return. In our algorithm the
RL agent utilizes the criticality measure, a function provided by a human
trainer, in order to locally choose the best stepnumber n for the update of the
Q function.
Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
Authors: Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi
Date: 2018-10-16 00:00:00
Tasks: Prediction, Video Prediction

Spatiotemporal sequence prediction is an important problem in deep learning.
We study next-frame(s) video prediction using a deep-learning-based predictive
coding framework that uses convolutional, long short-term memory (convLSTM)
modules. We introduce a novel reduced-gate convolutional LSTM (rgcLSTM)
architecture that requires a significantly lower parameter budget than a
comparable convLSTM. Our reduced-gate model achieves equal or better
next-frame(s) prediction accuracy than the original convolutional LSTM while
using a smaller parameter budget, thereby reducing training time. We tested our
reduced gate modules within a predictive coding architecture on the moving
MNIST and KITTI datasets. We found that our reduced-gate model has a
significant reduction of approximately 40 percent of the total number of
training parameters and a 25 percent redution in elapsed training time in
comparison with the standard convolutional LSTM model. This makes our model
more attractive for hardware implementation especially on small devices.