pwc-archive/papers-with-abstracts huggingface dataset:
Clinical Risk: wavelet reconstruction networks for marked point processes
Authors: Jeremy C. Weiss
Date: 2018-09-27 00:00:00
Tasks: Point Processes

Timestamped sequences of events, pervasive in domains with data logs, e.g., health records, are often modeled as point processes with rate functions over time. Leading classical methods for risk scores such as Cox and Hawkes processes use such data but make strong assumptions about the shape and form of multivariate influences, resulting in time-to-event distributions irreflective of many real world processes. Recent methods in point processes and recurrent neural networks capably model rate functions but may be complex and difficult to interrogate. Our work develops a high-performing, interrogable model.  We introduce wavelet reconstruction networks, a multivariate point process with a sparse wavelet reconstruction kernel to model rate functions from marked, timestamped data. We show they achieve improved performance and interrogability over baselines in forecasting complications and scheduled care visits in patients with diabetes.
Fast Binary Functional Search on Graph
Authors: Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li
Date: 2018-09-27 00:00:00
Tasks: Open-Ended Question Answering

The large-scale search is an essential task in modern information systems. Numerous learning based models are proposed to capture semantic level similarity measures for searching or ranking. However, these measures are usually complicated and beyond metric distances. As Approximate Nearest Neighbor Search (ANNS) techniques have specifications on metric distances, efficient searching by advanced measures is still an open question. In this paper, we formulate large-scale search as a general task, Optimal Binary Functional Search (OBFS), which contains ANNS as special cases. We analyze existing OBFS methods' limitations and explain they are not applicable for complicated searching measures. We propose a flexible graph-based solution for OBFS, Search on L2 Graph (SL2G). SL2G approximates gradient decent in Euclidean space, with accessible conditions. Experiments demonstrate SL2G's efficiency in searching by advanced matching measures (i.e., Neural Network based measures).
HyperGAN: Exploring the Manifold of Neural Networks
Authors: Neale Ratzlaff, Li  Fuxin
Date: 2018-09-27 00:00:00

We introduce HyperGAN, a generative network that learns to generate all the weight parameters of deep neural networks. HyperGAN first transforms low dimensional noise into a latent space, which can be sampled from to obtain diverse, performant sets of parameters for a target architecture. We utilize an architecture that bears resemblance to generative adversarial networks, but we evaluate the likelihood of samples with a classification loss. This is equivalent to minimizing the KL-divergence between the generated network parameter distribution and an unknown true parameter distribution. We apply HyperGAN to classification, showing that HyperGAN can learn to generate parameters which solve the MNIST and CIFAR-10 datasets with competitive performance to fully supervised learning while learning a rich distribution of effective parameters. We also show that HyperGAN can also provide better uncertainty than standard ensembles. This is evaluated by the ability of HyperGAN-generated ensembles to detect out of distribution data as well as adversarial examples. We see that in addition to being highly accurate on inlier data, HyperGAN can provide reasonable uncertainty estimates.
Correction Networks: Meta-Learning for Zero-Shot Learning
Authors: R. Lily Hu, Caiming Xiong, Richard Socher
Date: 2018-09-27 00:00:00
Tasks: Meta-Learning, Prediction, zero-shot-classification, Zero-Shot Learning

We propose a model that learns to perform zero-shot classification using a meta-learner that is trained to produce a correction to the output of a previously trained learner. The model consists of two modules: a task module that supplies an initial prediction, and a correction module that updates the initial prediction. The task module is the learner and the correction module is the meta-learner. The correction module is trained in an episodic approach whereby many different task modules are trained on various subsets of the total training data, with the rest being used as unseen data for the correction module. The correction module takes as input a representation of the task module's training data so that the predicted correction is a function of the task module's training data.  The correction module is trained to update the task module's prediction to be closer to the target value. This approach leads to state-of-the-art performance for zero-shot classification on natural language class descriptions on the CUB and NAB datasets.
NUTS: Network for Unsupervised Telegraphic Summarization
Authors: Chanakya Malireddy, Tirth Maniar, Sajal Maheshwari, Manish Shrivastava
Date: 2018-09-27 00:00:00
Tasks: Decoder, Extractive Summarization, Sentence

Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare well in domains like fictional narratives where there is no central theme and core information is not encapsulated by a small set of sentences. For the purpose of reducing the size of the document while conveying the idea expressed by each sentence, we need more sentence specific methods. Telegraphic summarization, which selects short segments across several sentences, is better suited for such domains. Telegraphic summarization captures the plot better by retaining shorter versions of each sentence while not really concerning itself with grammatically linking these segments. In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries.
We use multiple encoder-decoder networks and learn to drop portions of the text that are inferable from the chosen segments. The model is agnostic to both sentence length and style. We demonstrate that the summaries produced by our model show significant quantitative and qualitative improvement over those produced by existing methods and baselines.
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions
Authors: Daniel Elliott, Charles Anderson
Date: 2018-09-27 00:00:00
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning agents learn by exploring the environment and then exploiting what they have learned.
This frees the human trainers from having to know the preferred action or intrinsic value of each encountered state.
The cost of this freedom is reinforcement learning is slower and more unstable than supervised learning.
We explore the possibility that ensemble methods can remedy these shortcomings and do so by investigating a novel technique which harnesses the wisdom of the crowds by bagging Q-function approximator estimates.

Our results show that this proposed approach improves all three tasks and reinforcement learning approaches attempted.
We are able to demonstrate that this is a direct result of the increased stability of the action portion of the state-action-value function used by Q-learning to select actions and by policy gradient methods to train the policy.
Training generative latent models by variational f-divergence minimization
Authors: Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber
Date: 2018-09-27 00:00:00

Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of f-divergence between the model and data distribution. We derive an upper bound that holds for all f-divergences, showing the intuitive result that the divergence between two joint distributions is at least as great as the divergence between their corresponding marginals. Additionally, the f-divergence is not formally defined when two distributions have different supports. We thus propose a noisy version of f-divergence which is well defined in such situations. We demonstrate how the bound and the new version of f-divergence can be readily used to train complex probabilistic generative models of data and that the fitted model can depend significantly on the particular divergence used.
Improved Gradient Estimators for Stochastic Discrete Variables
Authors: Evgeny Andriyash, Arash Vahdat, Bill Macready
Date: 2018-09-27 00:00:00
Tasks: Variational Inference

In many applications we seek to optimize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribution parameters is a challenging problem. We analyze existing solutions including finite-difference (FD) estimators and continuous relaxation (CR) estimators in terms of bias and variance. We show that the commonly used Gumbel-Softmax estimator is biased and propose a simple method to reduce it. We also derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias. We demonstrate empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks.
AutoLoss: Learning Discrete Schedule for Alternate Optimization
Authors: Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019
Tasks: Image Generation, Machine Translation, Meta-Learning, NMT, Scheduling

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss, a meta-learning framework that automatically learns and determines the optimization schedule. AutoLoss provides a generic way to represent and learn the discrete optimization schedule from metadata, allows for a dynamic and data-driven schedule in ML problems that involve alternating updates of different parameters or from different loss objectives.

We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). We show that the AutoLoss controller is able to capture the distribution of better optimization schedules that result in higher quality of convergence on all four tasks. The trained AutoLoss controller is generalizable -- it can guide and improve the learning of a new task model with different specifications, or on different datasets.
Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
Authors: Nat Dilokthanakul, Nick Pawlowski, Murray Shanahan
Date: 2018-09-27 00:00:00
Tasks: Clustering

Deep latent variable models, such as variational autoencoders, have been successfully used to disentangle factors of variation in image datasets. The structure of the representations learned by such models is usually observed after training and iteratively refined by tuning the network architecture and loss function. Here we propose a method that can explicitly place information into a specific subset of the latent variables. We demonstrate the use of the method in a task of disentangling global structure from local features in images. One subset of the latent variables is encouraged to represent local features through an auxiliary modelling task. In this auxiliary task, the global structure of an image is destroyed by dividing it into pixel patches which are then randomly shuffled. The full set of latent variables is trained to model the original data, obliging the remainder of the latent representation to model the global structure. We demonstrate that this approach successfully disentangles the latent variables for global structure from local structure by observing the generative samples of SVHN and CIFAR10. We also clustering the disentangled global structure of SVHN and found that the emerging clusters represent meaningful groups of global structures – including digit identities and the number of digits presence. Finally, we discuss the problem of evaluating the clustering accuracy when ground truth categories are not expressive enough.
Convergence Properties of Deep Neural Networks on Separable Data
Authors: Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, Yoshua Bengio
Date: 2018-09-27 00:00:00
Tasks: Binary Classification

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.
Meta-Learning with Individualized Feature Space for Few-Shot Classification
Authors: Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
Date: 2018-09-27 00:00:00
Tasks: Classification, Meta-Learning, Model Optimization

Meta-learning provides a promising learning framework to address few-shot classification tasks. In existing meta-learning methods, the meta-learner is designed to learn about model optimization, parameter initialization, or similarity metric. Differently, in this paper, we propose to learn how to create an individualized feature embedding specific to a given query image for better classifying, i.e., given a query image, a specific feature embedding tailored for its characteristics is created accordingly, leading to an individualized feature space in which the query image can be more accurately classified.  Specifically, we introduce a kernel generator as meta-learner to learn to construct feature embedding for query images. The kernel generator acquires meta-knowledge of generating adequate convolutional kernels for different query images during training, which can generalize to unseen categories without fine-tuning. In two standard few-shot classification data sets, i.e. Omniglot, and \emph{mini}ImageNet, our method shows highly competitive performance.
Neural Regression Tree
Authors: Wenbo Zhao, Shahan Ali Memon, Bhiksha Raj, Rita Singh
Date: 2018-09-27 00:00:00
Tasks: Classification, regression

Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.
Learning Corresponded Rationales for Text Matching
Authors: Mo Yu, Shiyu Chang, Tommi S Jaakkola
Date: 2018-09-27 00:00:00
Tasks: Natural Language Inference, Question Answering, Text Matching

The ability to predict matches between two sources of text has a number of applications including natural language inference (NLI) and question answering (QA). While flexible neural models have become effective tools in solving these tasks, they are rarely transparent in terms of the mechanism that mediates the prediction. In this paper, we propose a self-explaining architecture where the model is forced to highlight, in a dependent manner, how spans of one side of the input match corresponding segments of the other side in order to arrive at the overall decision. The text spans are regularized to be coherent and concise, and their correspondence is captured explicitly. The text spans -- rationales -- are learned entirely as latent mechanisms, guided only by the distal supervision from the end-to-end task. We evaluate our model on both NLI and QA using three publicly available datasets.  Experimental results demonstrate quantitatively and qualitatively that our method delivers interpretable justification of the prediction without sacrificing state-of-the-art performance. Our code and data split will be publicly available.
Differentiable Expected BLEU for Text Generation
Authors: Wentao Wang, Zhiting Hu, Zichao Yang, Haoran Shi, Eric P. Xing
Date: 2018-09-27 00:00:00
Tasks: Image Captioning, Machine Translation, Text Generation, Translation

Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy. Such training objective shows a discrepancy from test criteria like the BLEU metric. Recent work optimizes expected BLEU under the model distribution using policy gradient, while such algorithm can suffer from high variance and become impractical. In this paper, we propose a new Differentiable Expected BLEU (DEBLEU) objective that permits direct optimization of neural generation models with gradient descent. We leverage the decomposability and sparsity of BLEU, and reformulate it with moderate approximations, making the evaluation of the objective and its gradient efficient, comparable to common cross-entropy loss. We further devise a simple training procedure with ground-truth masking and annealing for stable optimization. Experiments on neural machine translation and image captioning show our method significantly improves over both cross-entropy and policy gradient training.
Success at any cost: value constrained model-free continuous control
Authors: Steven Bohez, Abbas Abdolmaleki, Michael Neunert, Jonas Buchli, Nicolas Heess, Raia Hadsell
Date: 2018-09-27 00:00:00
Tasks: continuous-control, Continuous Control

Naively applying Reinforcement Learning algorithms to continuous control problems -- such as locomotion and robot control -- to maximize task reward often results in policies which rely on high-amplitude, high-frequency control signals, known colloquially as bang-bang control. While such policies can implement the optimal solution, particularly in simulated systems, they are often not desirable for real world systems since bang-bang control can lead to increased wear and tear and energy consumption and tends to excite undesired second-order dynamics. To counteract this issue, multi-objective optimization can be used to simultaneously optimize both the reward and some auxiliary cost that discourages undesired (e.g. high-amplitude) control. In principle, such an approach can yield the sought after, smooth, control policies. It can, however, be hard to find the correct trade-off between cost and return that results in the desired behavior. In this paper we propose a new constraint-based approach which defines a lower bound on the return while minimizing one or more costs (such as control effort). We employ Lagrangian relaxation to learn both (a) the parameters of a control policy that satisfies the desired constraints and (b) the Lagrangian multipliers for the optimization. Moreover, we demonstrate policy optimization which satisfies constraints either in expectation or in a per-step fashion, and we learn a single conditional policy that is able to dynamically change the trade-off between return and cost. We demonstrate the efficiency of our approach using a number of continuous control benchmark tasks as well as a realistic, energy-optimized quadruped locomotion task.
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
Authors: Whiyoung Jung, Giseung Park, Youngchul Sung
Date: 2018-09-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, a new interactive parallel learning scheme is proposed to enhance the performance of off-policy continuous-action reinforcement learning.  In the proposed interactive parallel learning scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information. The information of the best policy  is fused in a soft manner by constructing an augmented loss function for policy update to enlarge the overall search space by the multiple learners. The guidance by the previous best policy and the enlarged search space by the proposed interactive parallel learning scheme enable faster and better policy search in the policy parameter space. Working algorithms are constructed by  applying the proposed interactive parallel learning scheme to several off-policy reinforcement learning algorithms such as  the twin delayed deep deterministic (TD3) policy gradient algorithm and the soft actor-critic (SAC) algorithm, and numerical results show that the constructed IPE-enhanced algorithms outperform most of the current state-of-the-art reinforcement learning algorithms for continuous action control.
A Proposed Hierarchy of Deep Learning Tasks
Authors: Joel Hestness, Sharan Narang, Newsha Ardalani, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou, Gregory Diamos, Kenneth Church
Date: 2018-09-27 00:00:00
Tasks: Deep Learning

As the pace of deep learning innovation accelerates, it becomes increasingly important to organize the space of problems by relative difficultly.  Looking to other fields for inspiration, we see analogies to the Chomsky Hierarchy in computational linguistics and time and space complexity in theoretical computer science.

As a complement to prior theoretical work on the data and computational requirements of learning, this paper presents an empirical approach. We introduce a methodology for measuring validation error scaling with data and model size and test tasks in natural language, vision, and speech domains. We find that power-law validation error scaling exists across a breadth of factors and that model size scales sublinearly with data size, suggesting that simple learning theoretic models offer insights into the scaling behavior of realistic deep learning settings, and providing a new perspective on how to organize the space of  problems. 

We measure the power-law exponent---the "steepness" of the learning curve---and propose using this metric to sort problems by degree of difficulty.  There is no data like more data, but some tasks are more effective at taking advantage of more data.  Those that are more effective are easier on the proposed scale. 

Using this approach, we can observe that studied tasks in speech and vision domains scale faster than those in the natural language domain, offering insight into the observation that progress in these areas has proceeded more rapidly than in natural language.