pwc-archive/papers-with-abstracts huggingface dataset:
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
Authors: Sergio Valcarcel Macua, Aleksi Tukiainen, Daniel García-Ocaña Hernández, David Baldazo, Enrique Munoz de Cote, Santiago Zazo
Date: 2017-10-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL). Each agent has access to data from its local task only, but it aims to learn a policy that performs well on average for the whole set of tasks. During the learning process, agents communicate their value-policy parameters to their neighbors, diffusing the information across the network, so that they converge to a common policy, with no need for a central node. The method is scalable, since the computational and communication costs per agent grow with its number of neighbors. We derive Diff-DAC's from duality theory and provide novel insights into the standard actor-critic framework, showing that it is actually an instance of the dual ascent method that approximates the solution of a linear program. Experiments suggest that Diff-DAC can outperform the single previous distributed MRL approach (i.e., Dist-MTLPS) and even the centralized architecture.
Cuckoo Search: State-of-the-Art and Opportunities
Authors: Xin-She Yang, Suash Deb
Date: 2018-04-22 00:00:00

Since the development of cuckoo search (CS) by Yang and Deb in 2009, CS has
been applied in a diverse range of applications. This paper first outlines the
key features of the algorithm and its variants, and then briefly summarizes the
state-of-the-art developments in many applications. The opportunities for
further research are also identified.
code2vec: Learning Distributed Representations of Code
Authors: Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
Date: 2018-03-26 00:00:00

We present a neural model for representing snippets of code as continuous
distributed vectors ("code embeddings"). The main idea is to represent a code
snippet as a single fixed-length $\textit{code vector}$, which can be used to
predict semantic properties of the snippet. This is performed by decomposing
code to a collection of paths in its abstract syntax tree, and learning the
atomic representation of each path $\textit{simultaneously}$ with learning how
to aggregate a set of them. We demonstrate the effectiveness of our approach by
using it to predict a method's name from the vector representation of its body.
We evaluate our approach by training a model on a dataset of 14M methods. We
show that code vectors trained on this dataset can predict method names from
files that were completely unobserved during training. Furthermore, we show
that our model learns useful method name vectors that capture semantic
similarities, combinations, and analogies. Comparing previous techniques over
the same data set, our approach obtains a relative improvement of over 75%,
being the first to successfully predict method names based on a large,
cross-project, corpus. Our trained model, visualizations and vector
similarities are available as an interactive online demo at
http://code2vec.org. The code, data, and trained models are available at
https://github.com/tech-srl/code2vec.
Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks
Authors: Fusheng Hao, Jun Cheng, Lei Wang, Xinchao Wang, Jianzhong Cao, Xiping Hu, Dapeng Tao
Date: 2018-04-22 00:00:00
Tasks: image-classification, Image Classification

Discriminative features are critical for machine learning applications. Most
existing deep learning approaches, however, rely on convolutional neural
networks (CNNs) for learning features, whose discriminant power is not
explicitly enforced. In this paper, we propose a novel approach to train deep
CNNs by imposing the intra-class compactness and the inter-class separability,
so as to enhance the learned features' discriminant power. To this end, we
introduce anchors, which are predefined vectors regarded as the centers for
each class and fixed during training. Discriminative features are obtained by
constraining the deep CNNs to map training samples to the corresponding anchors
as close as possible. We propose two principles to select the anchors, and
measure the proximity of two points using the Euclidean and cosine distance
metric functions, which results in two novel loss functions. These loss
functions require no sample pairs or triplets and can be efficiently optimized
by batch stochastic gradient descent. We test the proposed method on three
benchmark image classification datasets and demonstrate its promising results.
A General Path-Based Representation for Predicting Program Properties
Authors: Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
Date: 2018-03-26 00:00:00

Predicting program properties such as names or expression types has a wide
range of applications. It can ease the task of programming and increase
programmer productivity. A major challenge when learning from programs is
$\textit{how to represent programs in a way that facilitates effective
learning}$.
  We present a $\textit{general path-based representation}$ for learning from
programs. Our representation is purely syntactic and extracted automatically.
The main idea is to represent a program using paths in its abstract syntax tree
(AST). This allows a learning model to leverage the structured nature of code
rather than treating it as a flat sequence of tokens.
  We show that this representation is general and can: (i) cover different
prediction tasks, (ii) drive different learning algorithms (for both generative
and discriminative models), and (iii) work across different programming
languages.
  We evaluate our approach on the tasks of predicting variable names, method
names, and full types. We use our representation to drive both CRF-based and
word2vec-based learning, for programs of four languages: JavaScript, Java,
Python and C\#. Our evaluation shows that our approach obtains better results
than task-specific handcrafted representations across different tasks and
programming languages.
Inducing and Embedding Senses with Scaled Gumbel Softmax
Authors: Fenfei Guo, Mohit Iyyer, Jordan Boyd-Graber
Date: 2018-04-22 00:00:00

Methods for learning word sense embeddings represent a single word with multiple sense-specific vectors. These methods should not only produce interpretable sense embeddings, but should also learn how to select which sense to use in a given context. We propose an unsupervised model that learns sense embeddings using a modified Gumbel softmax function, which allows for differentiable discrete sense selection. Our model produces sense embeddings that are competitive (and sometimes state of the art) on multiple similarity based downstream evaluations. However, performance on these downstream evaluations tasks does not correlate with interpretability of sense embeddings, as we discover through an interpretability comparison with competing multi-sense embeddings. While many previous approaches perform well on downstream evaluations, they do not produce interpretable embeddings and learn duplicated sense groups; our method achieves the best of both worlds.
An Outlyingness Matrix for Multivariate Functional Data Classification
Authors: Wenlin Dai, Marc G. Genton
Date: 2017-04-09 00:00:00
Tasks: Classification, General Classification, speech-recognition, Speech Recognition

The classification of multivariate functional data is an important task in
scientific research. Unlike point-wise data, functional data are usually
classified by their shapes rather than by their scales. We define an
outlyingness matrix by extending directional outlyingness, an effective measure
of the shape variation of curves that combines the direction of outlyingness
with conventional depth. We propose two classifiers based on directional
outlyingness and the outlyingness matrix, respectively. Our classifiers provide
better performance compared with existing depth-based classifiers when applied
on both univariate and multivariate functional data from simulation studies. We
also test our methods on two data problems: speech recognition and gesture
classification, and obtain results that are consistent with the findings from
the simulated data.
Learning Deep Mean Field Games for Modeling Large Population Behavior
Authors: Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha
Date: 2017-11-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning

We consider the problem of representing collective behavior of large
populations and predicting the evolution of a population distribution over a
discrete state space. A discrete time mean field game (MFG) is motivated as an
interpretable model founded on game theory for understanding the aggregate
effect of individual actions and predicting the temporal evolution of
population distributions. We achieve a synthesis of MFG and Markov decision
processes (MDP) by showing that a special MFG is reducible to an MDP. This
enables us to broaden the scope of mean field game theory and infer MFG models
of large real-world systems via deep inverse reinforcement learning. Our method
learns both the reward function and forward dynamics of an MFG from real data,
and we report the first empirical test of a mean field game model of a
real-world social media population.
Decoupled Networks
Authors: Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song
Date: 2018-04-22 00:00:00
Proceeding: CVPR 2018 6

Inner product-based convolution has been a central component of convolutional
neural networks (CNNs) and the key to learning visual representations. Inspired
by the observation that CNN-learned features are naturally decoupled with the
norm of features corresponding to the intra-class variation and the angle
corresponding to the semantic difference, we propose a generic decoupled
learning framework which models the intra-class variation and semantic
difference independently. Specifically, we first reparametrize the inner
product to a decoupled form and then generalize it to the decoupled convolution
operator which serves as the building block of our decoupled networks. We
present several effective instances of the decoupled convolution operator. Each
decoupled operator is well motivated and has an intuitive geometric
interpretation. Based on these decoupled operators, we further propose to
directly learn the operator from data. Extensive experiments show that such
decoupled reparameterization renders significant performance gain with easier
convergence and stronger robustness.
Link Prediction using Embedded Knowledge Graphs
Authors: Yelong Shen, Po-Sen Huang, Ming-Wei Chang, Jianfeng Gao
Date: 2016-11-14 00:00:00
Tasks: Knowledge Base Completion, Knowledge Graphs, Link Prediction, Prediction

Since large knowledge bases are typically incomplete, missing facts need to
be inferred from observed facts in a task called knowledge base completion. The
most successful approaches to this task have typically explored explicit paths
through sequences of triples. These approaches have usually resorted to
human-designed sampling procedures, since large knowledge graphs produce
prohibitively large numbers of possible paths, most of which are uninformative.
As an alternative approach, we propose performing a single, short sequence of
interactive lookup operations on an embedded knowledge graph which has been
trained through end-to-end backpropagation to be an optimized and compressed
version of the initial knowledge base. Our proposed model, called Embedded
Knowledge Graph Network (EKGN), achieves new state-of-the-art results on
popular knowledge base completion benchmarks.
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
Authors: Tiancheng Zhao, Kyusong Lee, Maxine Eskenazi
Date: 2018-04-22 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Dialogue Generation, Dialogue Interpretation, Representation Learning, Response Generation, Sentence, Text Generation

The encoder-decoder dialog model is one of the most prominent methods used to
build dialog systems in complex domains. Yet it is limited because it cannot
output interpretable actions as in traditional systems, which hinders humans
from understanding its generation process. We present an unsupervised discrete
sentence representation learning method that can integrate with any existing
encoder-decoder dialog models for interpretable response generation. Building
upon variational autoencoders (VAEs), we present two novel models, DI-VAE and
DI-VST that improve VAEs and can discover interpretable semantics via either
auto encoding or context predicting. Our methods have been validated on
real-world dialog datasets to discover semantic representations and enhance
encoder-decoder models with interpretable generation.
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server
Authors: Guoxin Cui, Jun Xu, Wei Zeng, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng
Date: 2018-04-22 00:00:00
Tasks: BIG-bench Machine Learning, Quantization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the most significant bottleneck in training large scale machine
learning models on parameter server (PS) is the communication overhead, because
it needs to frequently exchange the model gradients between the workers and
servers during the training iterations. Gradient quantization has been proposed
as an effective approach to reducing the communication volume. One key issue in
gradient quantization is setting the number of bits for quantizing the
gradients. Small number of bits can significantly reduce the communication
overhead while hurts the gradient accuracies, and vise versa. An ideal
quantization method would dynamically balance the communication overhead and
model accuracy, through adjusting the number bits according to the knowledge
learned from the immediate past training iterations. Existing methods, however,
quantize the gradients either with fixed number of bits, or with predefined
heuristic rules. In this paper we propose a novel adaptive quantization method
within the framework of reinforcement learning. The method, referred to as
MQGrad, formalizes the selection of quantization bits as actions in a Markov
decision process (MDP) where the MDP states records the information collected
from the past optimization iterations (e.g., the sequence of the loss function
values). During the training iterations of a machine learning algorithm, MQGrad
continuously updates the MDP state according to the changes of the loss
function. Based on the information, MDP learns to select the optimal actions
(number of bits) to quantize the gradients. Experimental results based on a
benchmark dataset showed that MQGrad can accelerate the learning of a large
scale deep neural network while keeping its prediction accuracies.
Efficient Large-Scale Domain Classification with Personalized Attention
Authors: Young-Bum Kim, Dongchan Kim, Anjishnu Kumar, Ruhi Sarikaya
Date: 2018-04-22 00:00:00
Tasks: Classification, domain classification, General Classification, Natural Language Understanding

In this paper, we explore the task of mapping spoken language utterances to
one of thousands of natural language understanding domains in intelligent
personal digital assistants (IPDAs). This scenario is observed for many
mainstream IPDAs in industry that allow third parties to develop thousands of
new domains to augment built-in ones to rapidly increase domain coverage and
overall IPDA capabilities. We propose a scalable neural model architecture with
a shared encoder, a novel attention mechanism that incorporates personalization
information and domain-specific classifiers that solves the problem
efficiently. Our architecture is designed to efficiently accommodate new
domains that appear in-between full model retraining cycles with a rapid
bootstrapping mechanism two orders of magnitude faster than retraining. We
account for practical constraints in real-time production systems, and design
to minimize memory footprint and runtime latency. We demonstrate that
incorporating personalization results in significantly more accurate domain
classification in the setting with thousands of overlapping domains.
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding
Authors: Young-Bum Kim, Dongchan Kim, Joo-Kyung Kim, Ruhi Sarikaya
Date: 2018-04-22 00:00:00
Proceeding: NAACL 2018 6
Tasks: domain classification, General Classification, Natural Language Understanding, Reranking, Spoken Language Understanding

Intelligent personal digital assistants (IPDAs), a popular real-life
application with spoken language understanding capabilities, can cover
potentially thousands of overlapping domains for natural language
understanding, and the task of finding the best domain to handle an utterance
becomes a challenging problem on a large scale. In this paper, we propose a set
of efficient and scalable neural shortlisting-reranking models for large-scale
domain classification in IPDAs. The shortlisting stage focuses on efficiently
trimming all domains down to a list of k-best candidate domains, and the
reranking stage performs a list-wise reranking of the initial k-best domains
with additional contextual information. We show the effectiveness of our
approach with extensive experiments on 1,500 IPDA domains.
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
Authors: Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu
Date: 2018-04-22 00:00:00

Online matching problems have garnered significant attention in recent years due to numerous applications in e-commerce, online advertisements, ride-sharing, etc. Many of them capture the uncertainty in the real world by including stochasticity in both the arrival process and the matching process. The Online Stochastic Matching with Timeouts problem introduced by Bansal, et al., (Algorithmica, 2012) models matching markets (e.g., E-Bay, Amazon). Buyers arrive from an independent and identically distributed (i.i.d.) known distribution on buyer profiles and can be shown a list of items one at a time. Each buyer has some probability of purchasing each item and a limit (timeout) on the number of items they can be shown. Bansal et al., (Algorithmica, 2012) gave a 0.12-competitive algorithm which was improved by Adamczyk, et al., (ESA, 2015) to 0.24. We present an online attenuation framework that uses an algorithm for offline stochastic matching as a black box. On the upper bound side, we show that this framework, combined with a black-box adapted from Bansal et al., (Algorithmica, 2012), yields an online algorithm which nearly doubles the ratio to 0.46. On the lower bound side, we show that no algorithm can achieve a ratio better than 0.632 using the standard LP for this problem. This framework has a high potential for further improvements since new algorithms for offline stochastic matching can directly improve the ratio for the online problem. Our online framework also has the potential for a variety of extensions. For example, we introduce a natural generalization: Online Stochastic Matching with Two-sided Timeouts in which both online and offline vertices have timeouts. Our framework provides the first algorithm for this problem achieving a ratio of 0.30. We once again use the algorithm of Adamczyk et al., (ESA, 2015) as a black-box and plug-it into our framework.
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
Authors: Tianyun Zhang, Shaokai Ye, Yi-Peng Zhang, Yanzhi Wang, Makan Fardad
Date: 2018-02-15 00:00:00
Tasks: Computational Efficiency

We present a systematic weight pruning framework of deep neural networks
(DNNs) using the alternating direction method of multipliers (ADMM). We first
formulate the weight pruning problem of DNNs as a constrained nonconvex
optimization problem, and then adopt the ADMM framework for systematic weight
pruning. We show that ADMM is highly suitable for weight pruning due to the
computational efficiency it offers. We achieve a much higher compression ratio
compared with prior work while maintaining the same test accuracy, together
with a faster convergence rate. Our models are released at
https://github.com/KaiqiZhang/admm-pruning
Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching
Authors: Xiao Yang, Miaosen Wang, Wei Wang, Madian Khabsa, Ahmed Awadallah, Daniel Kifer, C. Lee Giles
Date: 2018-04-22 00:00:00
Tasks: Answer Selection, General Classification, Question Answering

Community-based question answering (CQA) websites represent an important
source of information. As a result, the problem of matching the most valuable
answers to their corresponding questions has become an increasingly popular
research topic. We frame this task as a binary (relevant/irrelevant)
classification problem, and present an adversarial training framework to
alleviate label imbalance issue. We employ a generative model to iteratively
sample a subset of challenging negative samples to fool our classification
model. Both models are alternatively optimized using REINFORCE algorithm. The
proposed method is completely different from previous ones, where negative
samples in training set are directly used or uniformly down-sampled. Further,
we propose using Multi-scale Matching which explicitly inspects the correlation
between words and ngrams of different levels of granularity. We evaluate the
proposed method on SemEval 2016 and SemEval 2017 datasets and achieves
state-of-the-art or similar performance.
Learning Sentence Embeddings for Coherence Modelling and Beyond
Authors: Tanner Bohn, Yining Hu, Jinhang Zhang, Charles X. Ling
Date: 2018-04-22 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

We present a novel and effective technique for performing text coherence tasks while facilitating deeper insights into the data. Despite obtaining ever-increasing task performance, modern deep-learning approaches to NLP tasks often only provide users with the final network decision and no additional understanding of the data. In this work, we show that a new type of sentence embedding learned through self-supervision can be applied effectively to text coherence tasks while serving as a window through which deeper understanding of the data can be obtained. To produce these sentence embeddings, we train a recurrent neural network to take individual sentences and predict their location in a document in the form of a distribution over locations. We demonstrate that these embeddings, combined with simple visual heuristics, can be used to achieve performance competitive with state-of-the-art on multiple text coherence tasks, outperforming more complex and specialized approaches. Additionally, we demonstrate that these embeddings can provide insights useful to writers for improving writing quality and informing document structuring, and assisting readers in summarizing and locating information.