pwc-archive/papers-with-abstracts huggingface dataset:
Neural networks for inverse control with system priors
Authors: Anonymous
Date: 2020-10-23 00:00:00
Tasks: Retrieval

Controlling a physical system to behave in a desired manner requires a prior knowledge of the system, having access to a model or a collection of labeled data consisting of the desired inputs-outputs of the system. When the system is unknown or labeled data is not available or expensive to acquire, resorting to approaches that do not rely on the use of training data is inevitable. In this work, we propose an algorithm based on untrained neural networks that can be applied to a physical system in its most general form to obtain the required input that would result in a desired target output. We showcase the applicability of our algorithm to experimental phase-retrieval problems in the complex environment of a scattering medium whose input-output relation follows a nonlinear and slowly time-varying setting. We show that despite partial measurements of the system, comparable fidelity to that of fully-observed methods or supervised networks is achievable.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Authors: Anonymous
Date: 2020-10-23 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability Metric~(Wasserstein GANs). With $f$-divergence as the objective function, the discriminator essentially estimates the density ratio, and the estimated ratio proves useful in further improving the sample quality of the generator. However, how to leverage the information contained in the discriminator of Wasserstein GANs (WGAN) is less explored.  In this paper, we introduce the Discriminator Contrastive Divergence, which is well motivated by the property of WGAN's discriminator and the relationship between WGAN and energy-based model. Compared to standard GANs, where the generator is directly utilized to obtain new samples, our method proposes a semi-amortized generation procedure where the samples are produced with the generator's output as an initial state. Then several steps of Langevin dynamics are conducted using the gradient of the discriminator. We demonstrate the benefits of significantly improved generation on both synthetic data and several real-world image generation benchmarks.
Complex Skill Acquisition through Simple Skill Imitation Learning
Authors: Anonymous
Date: 2020-10-23 00:00:00
Tasks: Imitation Learning

Humans often think of complex tasks as combinations of simpler subtasks in order to learn those complex tasks more efficiently. For example, a backflip could be considered a combination of four subskills: jumping, tucking knees, rolling backwards, and thrusting arms downwards. Motivated by this line of reasoning, we propose a new algorithm that trains neural network policies on simple, easy-to-learn skills in order to cultivate latent spaces that accelerate imitation learning of complex, hard-to-learn skills. We focus on the case in which the complex task comprises a concurrent (and possibly sequential) combination of the simpler subtasks, and therefore our algorithm can be seen as a novel approach to concurrent hierarchical imitation learning. We evaluate our algorithm on difficult tasks in a high-dimensional environment and see that it consistently outperforms a state-of-the-art baseline in training speed and overall performance.
Risk Quantification in Deep MRI Reconstruction
Authors: Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John M. Pauly
Date: 2020-10-23 00:00:00
Tasks: MRI Reconstruction

Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data. In this study, we develop methods to address these concerns, utilizing a VAE as a probabilistic recovery algorithm for pediatric knee MR imaging. Through our use of SURE, which examines the end-to-end network Jacobian, we demonstrate a new and rigorous metric for assessing risk in medical image recovery that applies universally across model architectures.
Towards Neurally Augmented ALISTA
Authors: Freya Behrens, Jonathan Sauder, Peter Jung
Date: 2020-10-23 00:00:00
Tasks: compressed sensing

It is well-established that many iterative sparse reconstruction algorithms such as ISTA can be unrolled to yield a learnable neural network for improved empirical performance. Recently, ALISTA has been introduced, combining the strong empirical performance of a fully learned approach like LISTA, while retaining theoretical guarantees of classical compressed sensing algorithms and significantly reducing the number of parameters to learn. However, these parameters are trained to work in expectation, often leading to suboptimal reconstruction of individual targets.  In this work we therefore introduce Neurally-Augmented-ALISTA, which computes step sizes and thresholds individually for each target vector during reconstruction. This adaptive approach is theoretically motivated by revisiting the recovery guarantees of ALISTA and is able to outperform existing algorithms in sparse reconstruction.
Learning Spectral Regularizations for Linear Inverse Problems
Authors: Hartmut Bauermeister, Martin Burger, Michael Moeller
Date: 2020-10-23 00:00:00

One of the main challenges in linear inverse problems is that a majority of such problems are ill-posed in the sense that the solution does not depend on the data continuously. To analyze this effect and reestablish a continuous dependence, classical theory in Hilbert spaces largely relies on the analysis and manipulation of the singular values of the linear operator and its pseudoinverse with the goal of, on the one hand, keeping the singular values of the reconstruction operator bounded, and, on the other hand, approximating the pseudoinverse sufficiently well for a given noise level. While classical regularization methods manipulate the singular values via explicitly defined functions, this paper considers learning such parameter choice rules in such a way, that one obtains higher quality reconstruction results while still remaining in a setting of provably convergent spectral regularization methods. We discuss different ways of parametrizing our spectral regularization methods via neural networks, interpret existing feed forward networks in the setting of spectral regularization which can become provably convergent via an additional projection, and finally demonstrate their superiority in 1d numerical examples.
Bayesian Inference in Physics-Driven Problems with Adversarial Priors
Authors: Dhruv V Patel, Deep Ray, Harisankar Ramaswamy, Assad Oberai
Date: 2020-10-23 00:00:00
Tasks: Bayesian Inference

Generative adversarial networks (GANs) have found multiple applications in the solution of inverse problems in science and engineering. These applications are driven by the ability of these networks to learn complex distributions and to map the original feature space to a low-dimensional latent space. In this manuscript we consider the use of GANs as priors in physics-driven Bayesian inference problems. Within this approach the posterior distribution is learnt by mapping the problem to the latent space of the GAN and then using an HMC sampler for efficient sampling. We apply this approach to solving linear and nonlinear inverse problems, including an example with experimental data acquired from an application in biophysical imaging. Furthermore, we analyze the weak convergence of the approximate prior to the true prior and elucidate its dependence on the capacity of the network and the number of training samples.
Uncertainty-Driven Adaptive Sampling via GANs
Authors: Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher
Date: 2020-10-23 00:00:00
Tasks: Generative Adversarial Network, SSIM

We propose an adaptive sampling method for the linear model, driven by the uncertainty estimation with a generative adversarial network (GAN) model. Specifically, given a forward observation model that provides partial measurements $\vy$ about an unknown parameter $\x$, we show how to build a GAN model to estimate its posterior $p(\x|\y)$. We then leverage our approximate posterior to perform sequential adaptive sampling by actively selecting the measurement with the current maximal uncertainty. We empirically demonstrate that our posterior estimate contracts rapidly towards the correct mode, while outperforming the state-of-the-art approaches even for other criteria for which they are specifically trained, such as PSNR or SSIM.
Compressed Sensing with Invertible Generative Models and Dependent Noise
Authors: Jay Whang, Qi Lei, Alex Dimakis
Date: 2020-10-23 00:00:00
Tasks: compressed sensing, Denoising

We study image inverse problems with invertible generative priors, specifically normalizing flow models. Our formulation views the solution as the maximum a posteriori (MAP) estimate of the image given the measurements. Our general formulation allows for any differentiable noise model with long-range dependencies as well as non-linear differentiable forward operators. We establish theoretical recovery guarantees for denoising and compressed sensing under our framework. We also empirically validate our method on various inverse problems including 1-bit compressed sensing and denoising with highly structured noise patterns.
Deep Learning Initialized Phase Retrieval
Authors: Raunak Manekar, Zhong Zhuang, Kshitij Tayal, Vipin Kumar, Ju Sun
Date: 2020-10-23 00:00:00
Tasks: Deep Learning, Retrieval

Phase retrieval (PR) consists of estimating 2D or 3D objects from their Fourier magnitudes and takes a central place in scientific imaging. At present, most iterative methods for PR work well only when the initialization is close enough to the solution and fail otherwise. But there has been no general way of obtaining desired initialization. In this paper, we show that a carefully designed deep learning pipeline can consistently generate reliable initialization, so that the subsequent iterative methods can solve the PR problem and produce high-quality solutions. Technically, PR is an inverse problem containing three forward symmetries, and naive deployment of end-to-end deep learning for inverse problems yields poor initialization. We explain why the symmetries cause the learning difficulty and propose a novel strategy that substantially improves the estimation. Overall, the proposed method solves PR in regimes not accessible by the previous methods, and our work synergizes deep learning and traditional approaches to solve difficult scientific inverse problems.
Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
Authors: Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Zhong Zhuang, Vipin Kumar, Ju Sun
Date: 2020-10-23 00:00:00
Tasks: Retrieval

In many physical systems, inputs related by intrinsic system symmetries generate the same output. So when inverting such systems, an input is mapped to multiple symmetry-related outputs. This causes fundamental difficulty in tackling these inverse problems by the emerging end-to-end deep learning approach. Taking phase retrieval as an illustrative example, we show that careful symmetry breaking on the training data can help get rid of the difficulty and significantly improve learning performance on real data.
Learning to Sample MRI via Variational Information Maximization
Authors: Cagan Alkan, Morteza Mardani, Shreyas Vasanawala, John M. Pauly
Date: 2020-10-23 00:00:00
Tasks: Decoder

Accelerating MRI scans requires optimal sampling of k-space data. This is however a daunting task due to the discrete and non-convex nature of sampling optimization. To cope with this challenge, we put forth a novel deep learning framework that leverages uncertainty autoencoders to enable joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows {\it continuous} optimization of k-space samples on a non-Cartesian plane, while the decoder is a deep reconstruction network. Our approach is universal in a sense that it can be used with any reconstruction network. Experiments with knee MRI shows improved reconstruction quality of our data-driven sampling over the prevailing variable-density sampling.
Compressed Sensing with Approximate Priors via Conditional Resampling
Authors: Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price
Date: 2020-10-23 00:00:00
Tasks: compressed sensing, Diversity

We characterize the measurement complexity of compressed sensing of
signals drawn from a known prior distribution, even when the support
of the prior is the entire space (rather than, say, sparse vectors).
We show for Gaussian measurements and \emph{any} prior distribution
on the signal, that the conditional resampling estimator achieves
near-optimal recovery guarantees.  Moreover, this result is robust
to model mismatch, as long as the distribution estimate (e.g., from
an invertible generative model) is close to the true distribution in
Wasserstein distance. We implement the conditional resampling
estimator for deep generative priors using Langevin dynamics, and
empirically find that it produces accurate estimates with more
diversity than MAP.
Deep Learning for Plasma Tomography in Nuclear Fusion
Authors: Diogo R. Ferreira, Pedro J. Carvalho
Date: 2020-10-23 00:00:00
Tasks: Deep Learning

Tomography is arguably one of the most representative examples of an inverse problem, where the shape of an object must be reconstructed from its projections over a limited number of lines of sight. The regularization that must be imposed to solve such an ill-posed problem often results in iterative algorithms that are computationally expensive and do not meet the requirements of real-time applications. Deep learning offers a promising approach to perform such reconstruction with sufficient accuracy, while being several orders of magnitude faster, to the point that it becomes possible to use tomography in real-time. In this paper, we give an example of how real-time tomography based on deep learning is being used to reconstruct the plasma radiation profile in a nuclear fusion device. The availability of such profile in real-time allows setting up new alarms in the real-time control system, with a view towards anticipating plasma disruptions.
Human Adversarial QA: Did the Model Understand the Paragraph?
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Language Modeling, Language Modelling, Question Answering

Recently, adversarial attacks have become an important means of gauging the robustness of natural language models as training and testing set methodology has proved inadequate. In this paper we explore an evaluation based on human-in-the-loop adversarial example generation. These adversarial examples aid us in finding the loopholes in the models and give insights into their working. In the published work on adversarial question-answering, perturbations are made on the questions without changing the background context on which the question is based. In the current work, we examine the complementary idea of perturbing the background context while keeping the question constant. We analyze the state-of-the-art language model BERT for the task of question-answering on SQuAD dataset using novel adversarial examples crafted by humans exposing the weaknesses of the model. We present the typology of the successful attacks here as a baseline for stress-testing QA systems.
On the Utility of Active Instance Selection for Few-Shot Learning
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Active Learning, Few-Shot Learning

Few-shot learning aims to understand new concepts by transferring knowledge and utilizing very few randomly selected labeled samples. Instead of selecting these instances randomly, active learning provides a promising alternative. In this work, we investigate the effectiveness of actively identifying informative samples on the performance of few-shot learning models. We show that despite the fact that regular classification tasks with larger amounts of labeled data benefit from active learning approaches, these benefits do not reliably generalize to the few-shot learning task. We characterize the best possible active few-shot learning performance, by introducing Single-Instance-Oracle and Batch-Oracle as active methods that assume access to labels of the unlabeled pool and the test set, and show via these “upper bounds” that we do not have a significant room for improving few-shot models through actively selecting instances.
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Active Learning, text-classification, Text Classification

One of the main bottlenecks to extending deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels.  While many previous works have sought to alleviate this problem with weak supervision and data programming, rule and label noise prevent them from approaching fully-supervised performance. This work-in-progress provides a principled, AI-guided approach to improve rule-based and weakly supervised text classification by performing active learning not on individual data instances, but on entire labeling functions.  We argue that such a framework can guide users and subject matter experts to select labeling rules that expand label function coverage without sacrificing clarity.  Our experiments show that our framework, ReGAL, is able to generate coherent labeling rules while simultaneously obtaining state-of-the-art performance in weakly supervised text classification.
Value Alignment Verification
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Autonomous Driving

As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important that humans can verify these agents' trustworthiness and efficiently evaluate their performance and correctness. In this paper we formalize the problem of value alignment verification: how can a human efficiently test whether the goals and behavior of another agent are aligned with the human's values? We explore several different value alignment verification settings and provide foundational theory regarding value alignment verification. We study alignment verification problems with idealized human testers that know their own reward function as well as value alignment verification problems where the human tester has implicit values. Our theoretical results and our empirical results in both a discrete grid navigation domain and a continuous autonomous driving domain demonstrate that it is possible to synthesize highly efficient and accurate value alignment verification tests for certifying the alignment of autonomous agents.
Evidential Reasoning with Expert-Guided Machine Learning
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: BIG-bench Machine Learning, Fault Detection

Evidential reasoning aims to infer hidden causes from observed effects. In the context of fault detection, it is possible to trace the cause of anomalies by combining evidential reasoning with physical knowledge. However, experts may not have full knowledge of the physical systems, and data may not be large enough to learn from scratch. We develop an online evidential reasoning algorithm that blends machine learning with expert-provided physical knowledge about causal structure and functional form. The expert first represents possible causes and effects in the structure of a Bayesian Network, then provides physics-informed priors about the model relating failure modes to effects, allowing inference in the absence of strong supervision. As data are sampled from the physical system, predictions are generated using a quasi-Bayesian mixture of the expert's judgment and a data-driven estimate. With simulated datasets, we evaluate the conditions under which the system converges to correct causal inferences under weak supervision, and small amounts of strong supervision from the expert. We find that the approach is able to make accurate inferences with little or no data unless the expert's physical model is very incorrect or the signal to noise ratio across error modes is small.
Continuous and Interactive Factual Knowledge Learning in Verification Dialogues
Authors: Anonymous
Date: 2020-10-15 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12
Tasks: Knowledge Base Completion, World Knowledge

Knowledge bases (KBs) used in applications such as dialogue systems need to be continuously expanded in order to serve the users well. This process is known as knowledge base completion (KBC). A piece of knowledge or a fact is often represented as a triple (s, r, t), meaning that the entity s and the entity t have the relation r or are linked by r. KBC builds a model to infer missing facts from the existing ones in a given KB. Existing KBC research typically makes the closed-world assumption that to infer a new fact (s, r, t), it assumes that s, r and t are already in the KB, but are not linked. Clearly, this assumption is a serious limitation. In this paper, we eliminate this assumption and allow s, r and/or t to be unknown to the KB, which we call open-world knowledge base completion (OKBC). We focus on solving OKBC via user interactions, which enables the proposed system to potentially serve as an engine for learning new knowledge during dialogue. Experimental results show the effectiveness of the proposed approach.
Real-Time Visual Feedback for Educative Benchmark Creation : A Human-and-Metric-in-the-Loop Workflow
Authors: Anonymous
Date: 2020-10-16 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12

Recent research has shown that language models exploit `artifacts' in benchmarks to solve tasks, rather than truly learning them. Considering that this behavior inflates model performance, shouldn't the creation of better benchmarks be our priority? In pursuit of this, we focus on guiding crowdworkers, an under-explored facet of addressing benchmark idiosyncrasies. We propose VAIDA,  a novel benchmark creation paradigm for NLP. VAIDA provides realtime visual feedback to both crowdworkers and backend analysts on both sample and dataset quality, and aims to educate them on the same. VAIDA also facilitates sample correction to improve quality via recommendations. VAIDA is domain, model, task, and metric agnostic, and constitutes a paradigm shift for robust, validated, and dynamic benchmark creation via human-and-metric-in-the-loop workflows. We demonstrate VAIDA's effectiveness by leveraging a state-of-the-art data quality metric DQI over four datasets. We further evaluate via expert review and a user study with NASA TLX. We find that VAIDA decreases effort, frustration, mental, and temporal demand of crowdworkers and analysts, while simultaneously increasing the performance of both user groups.
CoRefi: A Crowd Sourcing Suite for Coreference Annotation
Authors: Anonymous
Date: 2020-10-15 00:00:00
Proceeding: NeurIPS Workshop HAMLETS 2020 12

Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and more efficient annotation, we present CoRefi, a web-based coreference annotation suite, oriented for crowdsourcing. Beyond the core coreference annotation tool, CoRefi provides guided onboarding for the task as well as a novel algorithm for a reviewing phase. CoRefi is open source and directly embeds into any website, including popular crowdsourcing platforms.