pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Dimensional Explanation of Reviews
Authors: Diego Antognini, Claudiu Musat, Boi Faltings
Date: 2019-09-25 00:00:00
Tasks: Multi-Task Learning, Sentiment Analysis

Neural models achieved considerable improvement for many natural language processing tasks, but they offer little transparency, and interpretability comes at a cost. In some domains, automated predictions without justifications have limited applicability. Recently, progress has been made regarding single-aspect sentiment analysis for reviews, where the ambiguity of a justification is minimal. In this context, a justification, or mask, consists of (long) word sequences from the input text, which suffice to make the prediction. Existing models cannot handle more than one aspect in one training and induce binary masks that might be ambiguous. In our work, we propose a neural model for predicting multi-aspect sentiments for reviews and generates a probabilistic multi-dimensional mask (one per aspect) simultaneously, in an unsupervised and multi-task learning manner. Our evaluation shows that on three datasets, in the beer and hotel domain, our model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable.
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization
Authors: Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz
Date: 2019-09-25 00:00:00

Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem. Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN’s recurrency matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability. Here, we instead suggest a regularization scheme that pushes part of the RNN’s latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales. We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.
SoftAdam: Unifying SGD and Adam for better stochastic gradient descent
Authors: Abraham J. Fetterman, Christina H. Kim, Joshua Albrecht
Date: 2019-09-25 00:00:00
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling

Abstract Stochastic gradient descent (SGD) and Adam are commonly used to optimize deep neural networks, but choosing one usually means making tradeoffs between speed, accuracy and stability. Here we present an intuition for why the tradeoffs exist as well as a method for unifying the two in a continuous way. This makes it possible to control the way models are trained in much greater detail. We show that for default parameters, the new algorithm equals or outperforms SGD and Adam across a range of models for image classification tasks and outperforms SGD for language modeling tasks.
Learning Good Policies By Learning Good Perceptual Models
Authors: Yilun Du, Phillip Isola
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Representation Learning

    Reinforcement learning (RL) has led to increasingly complex looking behavior in recent years. However, such complexity can be misleading and hides over-fitting. We find that visual representations may be a useful metric of complexity, and both correlates well objective optimization and causally effects reward optimization. We then propose curious representation learning (CRL) which allows us to use better visual representation learning algorithms to correspondingly increase visual representation in policy through an intrinsic objective on both simulated environments and transfer to real images. Finally, we show better visual representations induced by CRL allows us to obtain better performance on Atari without any reward than other curiosity objectives.
Informed Temporal Modeling via Logical Specification of Factorial LSTMs
Authors: Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner
Date: 2019-09-25 00:00:00

Consider a world in which events occur that involve various entities. Learning how to predict future events from patterns of past events becomes more difficult as we consider more types of events. Many of the patterns detected in the dataset by an ordinary LSTM will be spurious since the number of potential pairwise correlations, for example, grows quadratically with the number of events. We propose a type of factorial LSTM architecture where different blocks of LSTM cells are responsible for capturing different aspects of the world state. We use Datalog rules to specify how to derive the LSTM structure from a database of facts about the entities in the world. This is analogous to how a probabilistic relational model (Getoor & Taskar, 2007) specifies a recipe for deriving a graphical model structure from a database. In both cases, the goal is to obtain useful inductive biases by encoding informed independence assumptions into the model. We specifically consider the neural Hawkes process, which uses an LSTM to modulate the rate of instantaneous events in continuous time. In both synthetic and real-world domains, we show that we obtain better generalization by using appropriate factorial designs specified by simple Datalog programs.
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach
Authors: Shubham Gupta, Ambedkar Dukkipati
Date: 2019-09-25 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train. While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup. In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model. We model the environment, as seen by each of the agents, using separate but related Markov decision processes. We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference. MA-SAC can be employed in both cooperative and competitive settings. Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios. While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents
Authors: Jesse Zhang, Brian Cheung, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
Date: 2019-09-25 00:00:00
Tasks: Domain Adaptation, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure? This problem setting occurs frequently in real-world reinforcement learning scenarios such as a vehicle adapting to drive in a new city, or a robotic drone adapting a policy trained only in simulation. While learning without catastrophic failures is exceptionally difficult, prior experience can allow us to learn models that make this much easier. These models might not directly transfer to new settings, but can enable cautious adaptation that is substantially safer than na\"{i}ve adaptation as well as learning from scratch. Building on this intuition, we propose risk-averse domain adaptation (RADA). RADA works in two steps: it first trains probabilistic model-based RL agents in a population of source domains to gain experience and capture epistemic uncertainty about the environment dynamics. Then, when dropped into a new environment, it employs a pessimistic exploration policy, selecting actions that have the best worst-case performance as forecasted by the probabilistic model. We show that this simple maximin policy accelerates domain adaptation in a safety-critical driving environment with varying vehicle sizes. We compare our approach against other approaches for adapting to new environments, including meta-reinforcement learning.
Relative Pixel Prediction For Autoregressive Image Generation
Authors: Wang Ling, Chris Dyer, Lei Yu, Lingpeng Kong, Dani Yogatama, Susannah Young
Date: 2019-09-25 00:00:00
Tasks: Colorization, Image Colorization, Image Compression, Image Generation, Prediction, Super-Resolution, Unconditional Image Generation

In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding. In contrast, existing neural autoregressive image generation models predict the absolute pixel intensities at each position, which is a more challenging problem. In this paper, we propose to predict pixels relatively, by predicting new pixels relative to previously generated pixels (or pixels from the conditioning context, when available). We show that this form of prediction fare favorably to its absolute counterpart when used independently, but their coordination under an unified probabilistic model yields optimal performance, as the model learns to predict sharp transitions using the absolute predictor, while generating smooth transitions using the relative predictor.
Experiments on multiple benchmarks for unconditional image generation, image colorization, and super-resolution indicate that our presented mechanism leads to improvements in terms of likelihood compared to the absolute prediction counterparts.
A Greedy Approach to Max-Sliced Wasserstein GANs
Authors: András Horváth
Date: 2019-09-25 00:00:00

Generative Adversarial Networks have made data generation possible in various use cases, but in case of complex, high-dimensional distributions it can be difficult to train them, because of convergence problems and the appearance of mode collapse.
Sliced Wasserstein GANs and especially the application of the Max-Sliced Wasserstein distance made it possible to approximate Wasserstein distance during training in an efficient and stable way and helped ease convergence problems of these architectures.

This method transforms sample assignment and distance calculation into sorting the one-dimensional projection of the samples, which results a sufficient approximation of the high-dimensional Wasserstein distance. 

In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.
Symmetry and Systematicity
Authors: Jeff Mitchell, Jeff Bowers
Date: 2019-09-25 00:00:00

We argue that symmetry is an important consideration in addressing the problem
of systematicity and investigate two forms of symmetry relevant to symbolic processes. 
We implement this approach in terms of convolution and show that it can
be used to achieve effective generalisation in three toy problems: rule learning,
composition and grammar learning.
Towards Interpretable Evaluations: A Case Study of Named Entity Recognition
Authors: Jinlan Fu, PengFei Liu, Xuanjing Huang
Date: 2019-09-25 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

    With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 do not tell us \emph{why} or \emph{how} a particular method is better and how dataset biases influence the choices of model design.
    In this paper, we present a general methodology for {\emph{interpretable}} evaluation of NLP systems and choose the task of named entity recognition (NER) as a case study, which is a core task of identifying people, places, or organizations in text. The proposed evaluation method enables us to interpret the \textit{model biases}, \textit{dataset biases}, and how the \emph{differences in the datasets} affect the design of the models, identifying the strengths and weaknesses of current approaches. By making our {analysis} tool available, we make it easy for future researchers to run similar analyses and drive the progress in this area.
Bootstrapping the Expressivity with Model-based Planning
Authors: Kefan Dong, Yuping Luo, Tengyu Ma
Date: 2019-09-25 00:00:00
Tasks: model, MuJoCo

We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.  We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal $Q$-functions and policies are much more complex than the dynamics. We hypothesize many real-world MDPs also have a similar property. For these MDPs, model-based planning is a favorable algorithm, because the resulting policies can approximate the optimal policy significantly better than a neural network parameterization can, and model-free or model-based policy optimization rely on policy parameterization. Motivated by the theory, we apply a simple multi-step model-based bootstrapping planner (BOOTS) to bootstrap a weak $Q$-function into a stronger policy. Empirical results show that applying BOOTS on top of model-based or model-free policy optimization algorithms at the test time improves the performance on MuJoCo benchmark tasks.
Improving Multi-Manifold GANs with a Learned Noise Prior
Authors: Matthew Amodio, Smita Krishnaswamy
Date: 2019-09-25 00:00:00

Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution. For example, a single generator struggles to map continuous noise (e.g. a uniform distribution) to discontinuous output (e.g. separate Gaussians) or complex output (e.g. intersecting parabolas). We address this problem by learning to generate from multiple models such that the generator's output is actually the combination of several distinct networks. We contribute a novel formulation of multi-generator models where we learn a prior over the generators conditioned on the noise, parameterized by a neural network. Thus, this network not only learns the optimal rate to sample from each generator but also optimally shapes the noise received by each generator. The resulting Noise Prior GAN (NPGAN) achieves expressivity and flexibility that surpasses both single generator models and previous multi-generator models.
Iterative Target Augmentation for Effective Conditional Generation
Authors: Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola
Date: 2019-09-25 00:00:00
Tasks: Program Synthesis

Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs. However, available training data may not be sufficient for a generative model to learn all possible complex transformations. By leveraging the idea that evaluation is easier than generation, we show how a simple, broadly applicable, iterative target augmentation scheme can be surprisingly effective in guiding the training and use of such models. Our scheme views the generative model as a prior distribution, and employs a separately trained filter as the likelihood. In each augmentation step, we filter the model's outputs to obtain additional prediction targets for the next training epoch. Our method is applicable in the supervised as well as semi-supervised settings. We demonstrate that our approach yields significant gains over strong baselines both in molecular optimization and program synthesis. In particular, our augmented model outperforms the previous state-of-the-art in molecular optimization by over 10% in absolute gain.
Low Rank Training of Deep Neural Networks for Emerging Memory Technology
Authors: Albert Gural, Phillip Nadeau, Mehul Tikekar, Boris Murmann
Date: 2019-09-25 00:00:00
Tasks: Computational Efficiency, Decision Making, Federated Learning, Quantization

The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making "at the edge." However, this work has traditionally focused on neural network inference, rather than training, due to memory and compute limitations, especially in emerging non-volatile memory systems, where writes are energetically costly and reduce lifespan. Yet, the ability to train at the edge is becoming increasingly important as it enables applications such as real-time adaptability to device drift and environmental variation, user customization, and federated learning across devices. In this work, we address four key challenges for training on edge devices with non-volatile memory: low weight update density, weight quantization, low auxiliary memory, and online learning. We present a low-rank training scheme that addresses these four challenges while maintaining computational efficiency. We then demonstrate the technique on a representative convolutional neural network across several adaptation problems, where it out-performs standard SGD both in accuracy and in number of weight updates.
Cyclic Graph Dynamic Multilayer Perceptron for Periodic Signals
Authors: Mikio Furokawa, Erik Gest, Takayuki Hirano, Kamal Youcef-Toumi
Date: 2019-09-25 00:00:00

We propose a feature extraction for periodic signals. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. It is possible to collect data about periodicity by mea- suring a shaft’s rotation. However, it is difficult to perfectly control the collection timing of the measurements. Imprecise timing creates phase shifts in the resulting data. Although a phase shift does not materially affect the measurement of any given data point collected, it does alter the order in which all of the points are col- lected. It is difficult for classical methods, like multi-layer perceptron, to identify or quantify these alterations because they depend on the order of the input vectors’ components. This paper proposes a robust method for extracting features from phase shift data by adding a graph structure to each data point and constructing a suitable machine learning architecture for graph data with cyclic permutation. Simulation and experimental results illustrate its effectiveness.
Atomic Compression Networks
Authors: Jonas Falkner, Josif Grabocka, Lars Schmidt-Thieme
Date: 2019-09-25 00:00:00
Tasks: Model Compression

Compressed forms of deep neural networks are essential in deploying large-scale
computational models on resource-constrained devices. Contrary to analogous
domains where large-scale systems are build as a hierarchical repetition of small-
scale units, the current practice in Machine Learning largely relies on models with
non-repetitive components. In the spirit of molecular composition with repeating
atoms, we advance the state-of-the-art in model compression by proposing Atomic
Compression Networks (ACNs), a novel architecture that is constructed by recursive
repetition of a small set of neurons. In other words, the same neurons with the
same weights are stochastically re-positioned in subsequent layers of the network.
Empirical evidence suggests that ACNs achieve compression rates of up to three
orders of magnitudes compared to fine-tuned fully-connected neural networks (88×
to 1116× reduction) with only a fractional deterioration of classification accuracy
(0.15% to 5.33%). Moreover our method can yield sub-linear model complexities
and permits learning deep ACNs with less parameters than a logistic regression
with no decline in classification accuracy.
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed
Authors: Qingru Zhang, Yuhuai Wu, Fartash Faghri, Tianzong Zhang, Jimmy Ba
Date: 2019-09-25 00:00:00
Tasks: Computational Efficiency, regression, Stochastic Optimization

Stochastic gradient descent (SGD), which  trades off noisy gradient updates for computational efficiency, is the de-facto optimization algorithm to solve large-scale machine learning problems. SGD can make rapid learning progress by performing updates using subsampled training data, but the noisy updates also lead to slow asymptotic convergence.   Several variance reduction algorithms, such as SVRG, introduce control variates to obtain a lower variance gradient estimate and faster convergence.  Despite their appealing asymptotic guarantees, SVRG-like algorithms have not been widely adopted in deep learning. The traditional asymptotic analysis in stochastic optimization provides limited insight into training deep learning models under a fixed number of epochs. In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem. Our primary focus is to compare the exact loss of SVRG to that of SGD at each iteration t. We show that the learning dynamics of our regression model closely matches with that of neural networks on MNIST and CIFAR-10 for both the underparameterized and the overparameterized models. Our analysis and experimental results suggest there is a trade-off between the computational cost and the convergence speed in underparametrized neural networks. SVRG outperforms SGD after a few epochs in this regime. However, SGD is shown to always outperform SVRG in the overparameterized regime.
PAC-Bayesian Neural Network Bounds
Authors: Yossi Adi, Alex Schwing, Tamir Hazan
Date: 2019-09-25 00:00:00
Tasks: Generalization Bounds

Bayesian neural networks, which both use the negative log-likelihood loss function and average their predictions using a learned posterior over the parameters, have been used successfully across many scientific fields, partly due to their ability to `effortlessly' extract desired representations from many large-scale datasets. However, generalization bounds for this setting is still missing.
In this paper, we present a new PAC-Bayesian generalization bound for the negative log-likelihood loss which utilizes the \emph{Herbst Argument} for the log-Sobolev inequality to bound the moment generating function of the learners risk.