pwc-archive/papers-with-abstracts huggingface dataset:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Authors: Michael Oberst, David Sontag
Date: 2019-05-14 00:00:00
Tasks: counterfactual, Management, Off-policy evaluation

We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy. In particular, we introduce a class of structural causal models (SCMs) for generating counterfactual trajectories in finite partially observable Markov Decision Processes (POMDPs). We see this as a useful procedure for off-policy "debugging" in high-risk settings (e.g., healthcare); by decomposing the expected difference in reward between the RL and observed policy into specific episodes, we can identify episodes where the counterfactual difference in reward is most dramatic. This in turn can be used to facilitate review of specific episodes by domain experts. We demonstrate the utility of this procedure with a synthetic environment of sepsis management.
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference
Authors: Yujia Chen, Yang Lou, Kun Wang, Matthew A. Kupinski, Mark A. Anastasio
Date: 2019-05-14 00:00:00
Tasks: Bayesian Inference, Compressive Sensing, Image Reconstruction

It is widely accepted that optimization of imaging system performance should be guided by task-based measures of image quality (IQ). It has been advocated that imaging hardware or data-acquisition designs should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the measurement noise and class of objects to be imaged, without consideration of the reconstruction method. In practice, accurate and tractable models of the complete object statistics are often difficult to determine. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and sparse image reconstruction are innately coupled technologies. In this work, a sparsity-driven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated. The SDO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute the SDO test statistic, computational tools developed recently for variational Bayesian inference with sparse linear models are adopted. The use of the SDO to rank data-acquisition designs in a stylized example as motivated by magnetic resonance imaging (MRI) is demonstrated. This study reveals that the SDO can produce rankings that are consistent with visual assessments of the reconstructed images but different from those produced by use of the traditionally employed Hotelling observer (HO).
Ontology-Aware Clinical Abstractive Summarization
Authors: Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. Filice
Date: 2019-05-14 00:00:00
Tasks: Abstractive Text Summarization

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.
Curriculum Learning for Domain Adaptation in Neural Machine Translation
Authors: Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, Kevin Duh
Date: 2019-05-14 00:00:00
Proceeding: NAACL 2019 6
Tasks: Domain Adaptation, Machine Translation, Translation

We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
Authors: Md. Shad Akhtar, Dushyant Singh Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
Date: 2019-05-14 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decision Making, Emotion Recognition, Multi-Task Learning, Sentiment Analysis

Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e., text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI dataset for multi-modal sentiment and emotion analysis. Evaluation results suggest that multi-task learning framework offers improvement over the single-task framework. The proposed approach reports new state-of-the-art performance for both sentiment analysis and emotion analysis.
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates
Authors: Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson, Cameron Browne
Date: 2019-05-14 00:00:00
Tasks: Board Games

In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games.
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
Authors: Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
Date: 2019-05-14 00:00:00
Tasks: Mixture-of-Experts, Off-policy evaluation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local accuracy of each model and then using a planner to select which model to use at every time step as to minimize the return error estimate along entire trajectories. Across a variety of domains, our mixture-based approach outperforms the individual models alone as well as state-of-the-art importance sampling-based estimators.
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
Authors: Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid, Tim Menzies
Date: 2019-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, Hyperparameter Optimization

We assert that it is the ethical duty of software engineers to strive to reduce software discrimination. This paper discusses how that might be done. This is an important topic since machine learning software is increasingly being used to make decisions that affect people's lives. Potentially, the application of that software will result in fairer decisions because (unlike humans) machine learning software is not biased. However, recent results show that the software within many data mining packages exhibits "group discrimination"; i.e. their decisions are inappropriately affected by "protected attributes"(e.g., race, gender, age, etc.). There has been much prior work on validating the fairness of machine-learning models (by recognizing when such software discrimination exists). But after detection, comes mitigation. What steps can ethical software engineers take to reduce discrimination in the software they produce? This paper shows that making \textit{fairness} as a goal during hyperparameter optimization can (a) preserve the predictive power of a model learned from a data miner while also (b) generates fairer results. To the best of our knowledge, this is the first application of hyperparameter optimization as a tool for software engineers to generate fairer software.
Misleading Failures of Partial-input Baselines
Authors: Shi Feng, Eric Wallace, Jordan Boyd-Graber
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7
Tasks: Natural Language Inference, Visual Question Answering (VQA)

Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only models for SNLI or question-only models for VQA). When a partial-input baseline gets high accuracy, a dataset is cheatable. However, the converse is not necessarily true: the failure of a partial-input baseline does not mean a dataset is free of artifacts. To illustrate this, we first design artificial datasets which contain trivial patterns in the full input that are undetectable by any partial-input model. Next, we identify such artifacts in the SNLI dataset - a hypothesis-only model augmented with trivial patterns in the premise can solve 15% of the examples that are previously considered "hard". Our work provides a caveat for the use of partial-input baselines for dataset verification and creation.
Improving Model Training by Periodic Sampling over Weight Distributions
Authors: Samarth Tripathi, Jiayi Liu, Unmesh Kurup, Mohak Shah, Sauptik Dhar
Date: 2019-05-14 00:00:00

In this paper, we explore techniques centered around periodic sampling of model weights that provide convergence improvements on gradient update methods (vanilla \acs{SGD}, Momentum, Adam) for a variety of vision problems (classification, detection, segmentation). Importantly, our algorithms provide better, faster and more robust convergence and training performance with only a slight increase in computation time. Our techniques are independent of the neural network model, gradient optimization methods or existing optimal training policies and converge in a less volatile fashion with performance improvements that are approximately monotonic. We conduct a variety of experiments to quantify these improvements and identify scenarios where these techniques could be more useful.
Learning to Groove with Inverse Sequence Transformations
Authors: Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman
Date: 2019-05-14 00:00:00
Tasks: Generative Adversarial Network, Quantization

We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models. Though Seq2Seq models usually require painstakingly aligned corpora, we show that it is possible to adapt an approach from the Generative Adversarial Network (GAN) literature (e.g. Pix2Pix (Isola et al., 2017) and Vid2Vid (Wang et al. 2018a)) to sequences, creating large volumes of paired data by performing simple transformations and training generative models to plausibly invert these transformations. Music, and drumming in particular, provides a strong test case for this approach because many common transformations (quantization, removing voices) have clear semantics, and models for learning to invert them have real-world applications. Focusing on the case of drum set players, we create and release a new dataset for this purpose, containing over 13 hours of recordings by professional drummers aligned with fine-grained timing and dynamics information. We also explore some of the creative potential of these models, including demonstrating improvements on state-of-the-art methods for Humanization (instantiating a performance from a musical score).
Deep Neural Architecture Search with Deep Graph Bayesian Optimization
Authors: Lizheng Ma, Jiaxu Cui, Bo Yang
Date: 2019-05-14 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Graph Neural Network, Neural Architecture Search

Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks.
Online Anomaly Detection with Sparse Gaussian Processes
Authors: Jingjing Fei, Shiliang Sun
Date: 2019-05-14 00:00:00
Tasks: Anomaly Detection, Gaussian Processes, Time Series, Time Series Analysis

Online anomaly detection of time-series data is an important and challenging task in machine learning. Gaussian processes (GPs) are powerful and flexible models for modeling time-series data. However, the high time complexity of GPs limits their applications in online anomaly detection. Attributed to some internal or external changes, concept drift usually occurs in time-series data, where the characteristics of data and meanings of abnormal behaviors alter over time. Online anomaly detection methods should have the ability to adapt to concept drift. Motivated by the above facts, this paper proposes the method of sparse Gaussian processes with Q-function (SGP-Q). The SGP-Q employs sparse Gaussian processes (SGPs) whose time complexity is lower than that of GPs, thus significantly speeding up online anomaly detection. By using Q-function properly, the SGP-Q can adapt to concept drift well. Moreover, the SGP-Q makes use of few abnormal data in the training data by its strategy of updating training data, resulting in more accurate sparse Gaussian process regression models and better anomaly detection results. We evaluate the SGP-Q on various artificial and real-world datasets. Experimental results validate the effectiveness of the SGP-Q.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
Authors: Sheng Wan, Chen Gong, Ping Zhong, Bo Du, Lefei Zhang, Jian Yang
Date: 2019-05-14 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects.
Generic Encodings of Constructor Rewriting Systems
Authors: Horatiu Cirstea, Pierre-Etienne Moreau
Date: 2019-05-14 00:00:00
Tasks: Negation

Rewriting is a formalism widely used in computer science and mathematical logic. The classical formalism has been extended, in the context of functional languages, with an order over the rules and, in the context of rewrite based languages, with the negation over patterns. We propose in this paper a concise and clear algorithm computing the difference over patterns which can be used to define generic encodings of constructor term rewriting systems with negation and order into classical term rewriting systems. As a direct consequence, established methods used for term rewriting systems can be applied to analyze properties of the extended systems. The approach can also be seen as a generic compiler which targets any language providing basic pattern matching primitives. The formalism provides also a new method for deciding if a set of patterns subsumes a given pattern and thus, for checking the presence of useless patterns or the completeness of a set of patterns.
Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems
Authors: Karan Aggarwal, Uday Bondhugula
Date: 2019-05-14 00:00:00
Tasks: CPU, GPU, Tensor Decomposition

Sparse matrix-vector multiplication (SpMV) operations are commonly used in various scientific applications. The performance of the SpMV operation often depends on exploiting regularity patterns in the matrix. Various representations have been proposed to minimize the memory bandwidth bottleneck arising from the irregular memory access pattern involved. Among recent representation techniques, tensor decomposition is a popular one used for very large but sparse matrices. Post sparse-tensor decomposition, the new representation involves indirect accesses, making it challenging to optimize for multi-cores and GPUs. Computational neuroscience algorithms often involve sparse datasets while still performing long-running computations on them. The LiFE application is a popular neuroscience algorithm used for pruning brain connectivity graphs. The datasets employed herein involve the Sparse Tucker Decomposition (STD), a widely used tensor decomposition method. Using this decomposition leads to irregular array references, making it very difficult to optimize for both CPUs and GPUs. Recent codes of the LiFE algorithm show that its SpMV operations are the key bottleneck for performance and scaling. In this work, we first propose target-independent optimizations to optimize these SpMV operations, followed by target-dependent optimizations for CPU and GPU systems. The target-independent techniques include: (1) standard compiler optimizations, (2) data restructuring methods, and (3) methods to partition computations among threads. Then we present the optimizations for CPUs and GPUs to exploit platform-specific speed. Our highly optimized CPU code obtain a speedup of 27.12x over the original sequential CPU code running on 16-core Intel Xeon (Skylake-based) system, and our optimized GPU code achieves a speedup of 5.2x over a reference optimized GPU code version on NVIDIA's GeForce RTX 2080 Ti GPU.
Distributional Reinforcement Learning for Efficient Exploration
Authors: Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yao-Liang Yu
Date: 2019-05-13 00:00:00
Tasks: Atari Games, Distributional Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second is an exploration bonus calculated from the upper quantiles of the learned distribution. In Atari 2600 games, our method outperforms QR-DQN in 12 out of 14 hard games (achieving 483 \% average gain across 49 games in cumulative rewards over QR-DQN with a big win in Venture). We also compared our algorithm with QR-DQN in a challenging 3D driving simulator (CARLA). Results show that our algorithm achieves near-optimal safety rewards twice faster than QRDQN.
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
Authors: Wushi Dong, Murat Keceli, Rafael Vescovi, Hanyu Li, Corey Adams, Elise Jennings, Samuel Flender, Tom Uram, Venkatram Vishwanath, Nicola Ferrier, Narayanan Kasthuri, Peter Littlewood
Date: 2019-05-13 00:00:00
Tasks: Playing the Game of 2048

Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data. The flood-filling network (FFN) architecture has demonstrated leading performance for segmenting structures from this data. However, the training of the network is computationally expensive. In order to reduce the training time, we implemented synchronous and data-parallel distributed training using the Horovod library, which is different from the asynchronous training scheme used in the published FFN code. We demonstrated that our distributed training scaled well up to 2048 Intel Knights Landing (KNL) nodes on the Theta supercomputer. Our trained models achieved similar level of inference performance, but took less training time compared to previous methods. Our study on the effects of different batch sizes on FFN training suggests ways to further improve training efficiency. Our findings on optimal learning rate and batch sizes agree with previous works.
Semantic Search using Spreading Activation based on Ontology
Authors: Ngo Minh Vuong
Date: 2019-05-09 00:00:00
Tasks: Retrieval

Currently, the text document retrieval systems have many challenges in exploring the semantics of queries and documents. Each query implies information which does not appear in the query but the documents related with the information are also expected by user. The disadvantage of the previous spreading activation algorithms could be many irrelevant concepts added to the query. In this paper, a proposed novel algorithm is only activate and add to the query named entities which are related with original entities in the query and explicit relations in the query.