pwc-archive/papers-with-abstracts huggingface dataset:
NPA: Neural News Recommendation with Personalized Attention
Authors: Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie
Date: 2019-07-12 00:00:00
Tasks: Articles, Informativeness, News Recommendation

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.
MLR (Memory, Learning and Recognition): A General Cognitive Model -- applied to Intelligent Robots and Systems Control
Authors: Aras R. Dargazany
Date: 2019-07-12 00:00:00

This paper introduces a new perspective of intelligent robots and systems control. The presented and proposed cognitive model: Memory, Learning and Recognition (MLR), is an effort to bridge the gap between Robotics, AI, Cognitive Science, and Neuroscience. The currently existing gap prevents us from integrating the current advancement and achievements of these four research fields which are actively trying to define intelligence in either application-based way or in generic way. This cognitive model defines intelligence more specifically, parametrically and detailed. The proposed MLR model helps us create a general control model for robots and systems independent of their application domains and platforms since it is mainly based on the dataset provided for robots and systems controls. This paper is mainly proposing and introducing this concept and trying to prove this concept in a small scale, firstly through experimentation. The proposed concept is also applicable to other different platforms in real-time as well as in simulation.
Faster Neural Network Training with Data Echoing
Authors: Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
Date: 2019-07-12 00:00:00

In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline, such as disk I/O and data preprocessing, do not run on accelerators. As accelerators continue to improve, these earlier stages will increasingly become the bottleneck. In this paper, we introduce "data echoing," which reduces the total computation used by earlier pipeline stages and speeds up training whenever computation upstream from accelerators dominates the training time. Data echoing reuses (or "echoes") intermediate outputs from earlier pipeline stages in order to reclaim idle capacity. We investigate the behavior of different data echoing algorithms on various workloads, for various amounts of echoing, and for various batch sizes. We find that in all settings, at least one data echoing algorithm can match the baseline's predictive performance using less upstream computation. We measured a factor of 3.25 decrease in wall-clock time for ResNet-50 on ImageNet when reading training data over a network.
The Dynamic Embedded Topic Model
Authors: Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Date: 2019-07-12 00:00:00
Tasks: Articles, model, Topic Models, Variational Inference, Word Embeddings

Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We develop the dynamic embedded topic model (D-ETM), a generative model of documents that combines dynamic latent Dirichlet allocation (D-LDA) and word embeddings. The D-ETM models each word with a categorical distribution parameterized by the inner product between the word embedding and a per-time-step embedding representation of its assigned topic. The D-ETM learns smooth topic trajectories by defining a random walk prior over the embedding representations of the topics. We fit the D-ETM using structured amortized variational inference with a recurrent neural network. On three different corpora---a collection of United Nations debates, a set of ACL abstracts, and a dataset of Science Magazine articles---we found that the D-ETM outperforms D-LDA on a document completion task. We further found that the D-ETM learns more diverse and coherent topics than D-LDA while requiring significantly less time to fit.
Solving Hard Coreference Problems
Authors: Haoruo Peng, Daniel Khashabi, Dan Roth
Date: 2019-07-11 00:00:00
Proceeding: HLT 2015 5
Tasks: coreference-resolution, Coreference Resolution, Decision Making, Natural Language Understanding

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper, we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the state-of-the-art level on standard coreference resolution datasets.
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
Authors: Shuang Li, Gongguo Tang, Michael B. Wakin
Date: 2019-07-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Matrix Completion, Retrieval

The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and phase retrieval. In this work, we focus on the situation where the corresponding population risk is a degenerate non-convex loss function, namely, the Hessian of the population risk can have zero eigenvalues. Instead of analyzing the non-convex empirical risk directly, we first study the landscape of the corresponding population risk, which is usually easier to characterize, and then build a connection between the landscape of the empirical risk and its population risk. In particular, we establish a correspondence between the critical points of the empirical risk and its population risk without the strongly Morse assumption, which is required in existing literature but not satisfied in degenerate scenarios. We also apply the theory to matrix sensing and phase retrieval to demonstrate how to infer the landscape of empirical risk from that of the corresponding population risk.
Graph-Structured Visual Imitation
Authors: Maximilian Sieb, Zhou Xian, Audrey Huang, Oliver Kroemer, Katerina Fragkiadaki
Date: 2019-07-11 00:00:00

We cast visual imitation as a visual correspondence problem. Our robotic agent is rewarded when its actions result in better matching of relative spatial configurations for corresponding visual entities detected in its workspace and teacher's demonstration. We build upon recent advances in Computer Vision,such as human finger keypoint detectors, object detectors trained on-the-fly with synthetic augmentations, and point detectors supervised by viewpoint changes and learn multiple visual entity detectors for each demonstration without human annotations or robot interactions. We empirically show the proposed factorized visual representations of entities and their spatial arrangements drive successful imitation of a variety of manipulation skills within minutes, using a single demonstration and without any environment instrumentation. It is robust to background clutter and can effectively generalize across environment variations between demonstrator and imitator, greatly outperforming unstructured non-factorized full-frame CNN encodings of previous works.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
Authors: Alexandros Papangelis, Yi-Chia Wang, Piero Molino, Gokhan Tur
Date: 2019-07-11 00:00:00
Proceeding: WS 2019 9
Tasks: Natural Language Understanding, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. We model the interaction as a stochastic collaborative game where each agent (player) has a role ("assistant", "tourist", "eater", etc.) and their own objectives, and can only interact via natural language they generate. Each agent, therefore, needs to learn to operate optimally in an environment with multiple sources of uncertainty (its own NLU and NLG, the other agent's NLU, Policy, and NLG). In our evaluation, we show that the stochastic-game agents outperform deep learning based supervised baselines.
Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
Authors: Saeedeh Parsaeefard, Iman Tabrizian, Alberto Leon-Garcia
Date: 2019-07-11 00:00:00
Tasks: Cloud Computing, Management

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.
Online Learning to Estimate Warfarin Dose with Contextual Linear Bandits
Authors: Hai Xiao
Date: 2019-07-11 00:00:00

Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dose, then doctor closely monitor patient response and adjust accordingly to the correct dosage. The three commonly used strategies for an initial dosage are the fixed-dose approach, the Warfarin Clinical algorithm, and the Pharmacogenetic algorithm developed by the IWPC (International Warfarin Pharmacogenetics Consortium). It is always best to prescribe correct initial dosage, motivated by this challenge, this work explores the performance of multi-armed bandit algorithms to best predict the correct dosage of Warfarin instead of trial-and-error procedure. Real data from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is used, with it a series of linear bandit algorithms and variants are developed and evaluated on Warfarin dataset. All proposed algorithms outperformed the fixed-dose baseline algorithm, and some even matched up the Warfarin Clinical Dosing Algorithm. In addition, a few promising future directions are given for further exploration and development.
Mobility restores the mechanism which supports cooperation in the voluntary prisoner's dilemma game
Authors: Marcos Cardinot, Colm O'Riordan, Josephine Griffith, Attila Szolnoki
Date: 2019-07-11 00:00:00

It is generally believed that in a situation where individual and collective interests are in conflict, the availability of optional participation is a key mechanism to maintain cooperation. Surprisingly, this effect is sensitive to the use of microscopic dynamics and can easily be broken when agents make a fully rational decision during their strategy updates. In the framework of the celebrated prisoner's dilemma game, we show that this discrepancy can be fixed automatically if we leave the strict and frequently artifact condition of a fully occupied interaction graph, and allow agents to change not just their strategies but also their positions according to their success. In this way, a diluted graph where agents may move offers a natural and alternative way to handle artifacts arising from the application of specific and sometimes awkward microscopic rules.
Minimizers of the Empirical Risk and Risk Monotonicity
Authors: Marco Loog, Tom Viering, Alexander Mey
Date: 2019-07-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Density Estimation

Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a better understanding of the generalization properties of this learner. The behavior of learning curves is, however, not very well understood and can display (for most researchers) quite unexpected behavior. Our work introduces the formal notion of \emph{risk monotonicity}, which asks the risk to not deteriorate with increasing training set sizes in expectation over the training samples. We then present the surprising result that various standard learners, specifically those that minimize the empirical risk, can act \emph{non}monotonically irrespective of the training sample size. We provide a theoretical underpinning for specific instantiations from classification, regression, and density estimation. Altogether, the proposed monotonicity notion opens up a whole new direction of research.
Grounding Value Alignment with Ethical Principles
Authors: Tae Wan Kim, Thomas Donaldson, John Hooker
Date: 2019-07-11 00:00:00

An important step in the development of value alignment (VA) systems in AI is understanding how values can interrelate with facts. Designers of future VA systems will need to utilize a hybrid approach in which ethical reasoning and empirical observation interrelate successfully in machine behavior. In this article we identify two problems about this interrelation that have been overlooked by AI discussants and designers. The first problem is that many AI designers commit inadvertently a version of what has been called by moral philosophers the "naturalistic fallacy," that is, they attempt to derive an "ought" from an "is." We illustrate when and why this occurs. The second problem is that AI designers adopt training routines that fail fully to simulate human ethical reasoning in the integration of ethical principles and facts. Using concepts of quantified modal logic, we proceed to offer an approach that promises to simulate ethical reasoning in humans by connecting ethical principles on the one hand and propositions about states of affairs on the other.
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Authors: Gabriel Ilharco, Vihan Jain, Alexander Ku, Eugene Ie, Jason Baldridge
Date: 2019-07-11 00:00:00
Tasks: Dynamic Time Warping, Navigate, Reinforcement Learning, Time Series, Time Series Analysis

In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation metrics used thus far fail to properly account for the latter, relying instead on insufficient similarity comparisons. We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents. For such, we define the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efficiently calculated. Further, we define SDTW, which constrains nDTW to only successful paths. We collect human similarity judgments for simulated paths and find nDTW correlates better with human rankings than all other metrics. We also demonstrate that using nDTW as a reward signal for Reinforcement Learning navigation agents improves their performance on both the Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in particular highlight the superiority of SDTW over previous success-constrained metrics.
On the Optimality of Trees Generated by ID3
Authors: Alon Brutzkus, Amit Daniely, Eran Malach
Date: 2019-07-11 00:00:00

Since its inception in the 1980s, ID3 has become one of the most successful and widely used algorithms for learning decision trees. However, its theoretical properties remain poorly understood. In this work, we introduce a novel metric of a decision tree algorithm's performance, called mean iteration statistical consistency (MIC), which measures optimality of trees generated by ID3. As opposed to previous metrics, MIC can differentiate between different decision tree algorithms and compare their performance. We provide theoretical and empirical evidence that the TopDown variant of ID3, introduced by Kearns and Mansour (1996), has near-optimal MIC in various settings for learning read-once DNFs under product distributions. In contrast, another widely used variant of ID3 has MIC which is not near-optimal. We show that the MIC analysis predicts well the performance of these algorithms in practice. Our results present a novel view of decision tree algorithms which may lead to better and more practical guarantees for these algorithms.
Learning Key-Value Store Design
Authors: Stratos Idreos, Niv Dayan, Wilson Qin, Mali Akmanalp, Sophie Hilgard, Andrew Ross, James Lennon, Varun Jain, Harshita Gupta, David Li, Zichen Zhu
Date: 2019-07-11 00:00:00
Tasks: Layout Design

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs. The core intuition behind the construction of design continuums is that all data structures arise from the very same set of fundamental design principles, i.e., a small set of data layout design concepts out of which we can synthesize any design that exists in the literature as well as new ones. We show how to construct, evaluate, and expand, design continuums and we also present the first continuum that unifies major data structure designs, i.e., B+tree, B-epsilon-tree, LSM-tree, and LSH-table. The practical benefit of a design continuum is that it creates a fast inference engine for the design of data structures. For example, we can predict near instantly how a specific design change in the underlying storage of a data system would affect performance, or reversely what would be the optimal data structure (from a given set of designs) given workload characteristics and a memory budget. In turn, these properties allow us to envision a new class of self-designing key-value stores with a substantially improved ability to adapt to workload and hardware changes by transitioning between drastically different data structure designs to assume a diverse set of performance properties at will.
Predicting engagement in online social networks: Challenges and opportunities
Authors: Farig Sadeque, Steven Bethard
Date: 2019-07-11 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Domain Adaptation, Transfer Learning

Since the introduction of social media, user participation or engagement has received little research attention. In this survey article, we establish the notion of participation in social media and main challenges that researchers may face while exploring this phenomenon. We surveyed a handful of research articles that had been done in this area, and tried to extract, analyze and summarize the techniques performed by the researchers. We classified these works based on our task definitions, and explored the machine learning models that have been used for any kind of participation prediction. We also explored the vast amount of features that have been proven useful, and classified them into categories for better understanding and ease of re-implementation. We have found that the success of a technique mostly depends on the type of the network that has been researched on, and there is no universal machine learning algorithm or feature sets that works reasonably well in all types of social media. There is a lack of attempts in implementing state-of-the-art machine learning techniques like neural networks, and the possibility of transfer learning and domain adaptation has not been explored.