pwc-archive/papers-with-abstracts huggingface dataset:
A⋆MCTS: SEARCH WITH THEORETICAL GUARANTEE USING POLICY AND VALUE FUNCTIONS
Authors: Xian Wu, Yuandong Tian, Lexing Ying
Date: 2019-09-25 00:00:00
Tasks: Board Games

Combined with policy and value neural networks, Monte Carlos Tree Search (MCTS) is a critical component of the recent success of AI agents in learning to play board games like Chess and Go (Silver et al., 2017). However, the theoretical foundations of MCTS with policy and value networks remains open. Inspired by MCTS, we propose A⋆MCTS, a novel search algorithm that uses both the policy and value predictors to guide search and enjoys theoretical guarantees. Specifically, assuming that value and policy networks give reasonably accurate signals of the values of each state and action, the sample complexity (number of calls to the value network) to estimate the value of the current state, as well as the optimal one-step action to take from the current state, can be bounded. We apply our theoretical framework to different models for the noise distribution of the policy and value network as well as the distribution of rewards, and show that for these general models, the sample complexity is polynomial in D, where D is the depth of the search tree. Empirically, our method outperforms MCTS in these models.
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS
Authors: Eunju Cha, Jaeduck Jang, Junho Lee, Eunha Lee, Jong Chul Ye
Date: 2019-09-25 00:00:00
Tasks: Decoder, Denoising, Prediction

Encoder-decoder convolutional neural networks  (CNN)  have been extensively used for various  inverse problems. However,  their prediction error  for unseen test data is difficult to estimate  a priori, since the neural networks are trained using only selected data and their architectures are largely considered blackboxes. This poses a fundamental  challenge in improving the performance of  neural networks. Recently, it was shown that Stein’s unbiased risk estimator (SURE) can be used as an unbiased estimator of the prediction error for denoising problems. However, the computation of the divergence term in SURE is difficult to implement in a neural network framework, and the condition to avoid trivial identity mapping is not well defined. In this paper, inspired by the finding that  an encoder-decoder CNN can be expressed as a piecewise linear representation, we provide a  close form expression of  the unbiased estimator for the prediction error. The close form representation leads to a novel boosting scheme to prevent a neural network from converging to an identity mapping so that it can enhance the performance. Experimental results show that the proposed algorithm provides consistent improvement in various inverse problems.
Unsupervised Spatiotemporal Data Inpainting
Authors: Yuan Yin, Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari
Date: 2019-09-25 00:00:00
Tasks: Generative Adversarial Network

We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner. We place ourselves in the setting where there is neither access to paired nor unpaired training data. We consider several cases in which the underlying information of the observed sequence in certain areas is lost through an observation operator. In this case, the only available information is provided by the observation of the sequence, the nature of the measurement process and its associated statistics. We propose an unsupervised-learning framework to retrieve the most probable sequence using a generative adversarial network. We demonstrate the capacity of our model to exhibit strong reconstruction capacity on several video datasets such as satellite sequences or natural videos.
Deep geometric matrix completion: Are we doing it right?
Authors: Amit Boyarski, Sanketh Vedula, Alex Bronstein
Date: 2019-09-25 00:00:00
Tasks: Matrix Completion, Recommendation Systems

We address the problem of reconstructing a matrix from a subset of its entries. Current methods, branded as geometric matrix completion, augment classical rank regularization techniques by incorporating geometric information into the solution. This information is usually provided as graphs encoding relations between rows/columns.
In this work we propose a simple spectral approach for solving the matrix completion problem, via the framework of functional maps. We introduce the zoomout loss, a multiresolution spectral geometric loss inspired by recent advances in shape correspondence, whose minimization leads to state-of-the-art results on various recommender systems datasets. Surprisingly, for some datasets we were able to achieve comparable results even without incorporating geometric information. This puts into question both the quality of such information and current methods' ability to use it in a meaningful and efficient way.
Semantics Preserving Adversarial Attacks
Authors: Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
Date: 2019-09-25 00:00:00

While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a framework to generate semantics preserving adversarial examples. First, we present a manifold learning method to capture the semantics of the inputs. The motivating principle is to learn the low-dimensional geometric summaries of the inputs via statistical inference. Then, we perturb the elements of the learned manifold using the Gram-Schmidt process to induce the perturbed elements to remain in the manifold. To produce adversarial examples, we propose an efficient algorithm whereby we leverage the semantics of the inputs as a source of knowledge upon which we impose adversarial constraints. We apply our approach on toy data, images and text, and show its effectiveness in producing semantics preserving adversarial examples which evade existing defenses against adversarial attacks.
Symmetric-APL Activations: Training Insights and Robustness to Adversarial Attacks
Authors: Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi
Date: 2019-09-25 00:00:00

Deep neural networks with learnable activation functions have shown superior performance over deep neural networks with fixed activation functions for many different problems. The adaptability of learnable activation functions adds expressive power to the model which results in better performance. Here, we propose a new learnable activation function based on Adaptive Piecewise Linear units (APL), which 1) gives equal expressive power to both the positive and negative halves on the input space and 2) is able to approximate any zero-centered continuous non-linearity in a closed interval. We investigate how the shape of the Symmetric-APL function changes during training and perform ablation studies to gain insight into the reason behind these changes. We hypothesize that these activation functions go through two distinct stages: 1) adding gradient information and 2) adding expressive power. Finally, we show that the use of Symmetric-APL activations can significantly increase the robustness of deep neural networks to adversarial attacks. Our experiments on both black-box and open-box adversarial attacks show that commonly-used architectures, namely Lenet, Network-in-Network, and ResNet-18 can be up to 51% more resistant to adversarial fooling by only using the proposed activation functions instead of ReLUs.
Temporal Probabilistic Asymmetric Multi-task Learning
Authors: Nguyen Anh Tuan, Hyewon Jeong, Eunho Yang, Sungju Hwang
Date: 2019-09-25 00:00:00
Tasks: Multi-Task Learning, Prediction, Time Series, Time Series Analysis, Transfer Learning

When performing multi-task predictions with time-series data, knowledge learned for one task at a specific time step may be useful in learning for another task at a later time step (e.g. prediction of sepsis may be useful for prediction of mortality for risk prediction at intensive care units). To capture such dynamically changing asymmetric relationships between tasks and long-range temporal dependencies in time-series data, we propose a novel temporal asymmetric multi-task learning model, which learns to combine features from other tasks at diverse timesteps for the prediction of each task. One crucial challenge here is deciding on the direction and the amount of knowledge transfer, since loss-based knowledge transfer Lee et al. (2016; 2017) does not apply in our case where we do not have loss at each timestep. We propose to tackle this challenge by proposing a novel uncertainty- based probabilistic knowledge transfer mechanism, such that we perform knowledge transfer from more certain tasks with lower variance to uncertain ones with higher variance. We validate our Temporal Probabilistic Asymmetric Multi-task Learning (TP-AMTL) model on two clinical risk prediction tasks against recent deep learning models for time-series analysis, which our model significantly outperforms by successfully preventing negative transfer. Further qualitative analysis of our model by clinicians suggests that the learned knowledge transfer graphs are helpful in analyzing the model’s predictions.
Stablizing Adversarial Invariance Induction by Discriminator Matching
Authors: Yusuke Iwasawa, Kei Akuzawa, Yutaka Matsuo
Date: 2019-09-25 00:00:00
Tasks: Attribute, Domain Generalization, Fairness, Representation Learning

Incorporating the desired invariance into representation learning is a key challenge in many situations, e.g., for domain generalization and privacy/fairness constraints. An adversarial invariance induction (AII) shows its power on this purpose, which maximizes the proxy of the conditional entropy between representations and attributes by adversarial training between an attribute discriminator and feature extractor. However, the practical behavior of AII is still unclear as the previous analysis assumes the optimality of the attribute classifier, which is rarely held in practice. This paper first analyzes the practical behavior of AII both theoretically and empirically, indicating that AII has theoretical difficulty as it maximizes variational {\em upper} bound of the actual conditional entropy, and AII catastrophically fails to induce invariance even in simple cases as suggested by the above theoretical findings. We then argue that a simple modification to AII can significantly stabilize the adversarial induction framework and achieve better invariant representations. Our modification is based on the property of conditional entropy; it is maximized if and only if the divergence between all pairs of marginal distributions over $z$ between different attributes is minimized. The proposed method, {\em invariance induction by discriminator matching}, modify AII objective to explicitly consider the divergence minimization requirements by defining a proxy of the divergence by using the attribute discriminator. Empirical validations on both the toy dataset and four real-world datasets (related to applications of user anonymization and domain generalization) reveal that the proposed method provides superior performance when inducing invariance for nuisance factors.
Revisiting Gradient Episodic Memory for Continual Learning
Authors: Zhiyi Chen, Tong Lin*
Date: 2019-09-25 00:00:00
Tasks: Continual Learning, Transfer Learning

Gradient Episodic Memory (GEM) is an effective model for continual learning, where each gradient update for the current task is formulated as a quadratic program problem with inequality constraints that alleviate catastrophic forgetting of previous tasks. However, practical use of GEM is impeded by several limitations: (1) the data examples stored in the episodic memory may not be representative of past tasks; (2)  the inequality constraints appear to be rather restrictive for competing or conflicting tasks; (3) the inequality constraints can only avoid catastrophic forgetting but can not assure positive backward transfer. To address these issues, in this paper we aim at improving the original GEM model via three handy techniques without extra computational cost. Experiments on MNIST Permutations and incremental CIFAR100 datasets demonstrate that our techniques enhance the performance of GEM remarkably. On CIFAR100 the average accuracy is improved from 66.48% to 68.76%, along with the backward (knowledge) transfer growing from 1.38% to 4.03%.
Quantifying uncertainty with GAN-based priors
Authors: Dhruv V. Patel, Assad A. Oberai
Date: 2019-09-25 00:00:00
Tasks: Bayesian Inference, Generative Adversarial Network

Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to characterize mathematically. In this work we demonstrate how the approximate distribution learned by a generative adversarial network (GAN) may be used as a prior in a Bayesian update to address both these challenges. We demonstrate the efficacy of this approach by inferring and quantifying uncertainty in inference problems arising in computer vision and  physics-based applications. In both instances we highlight the role of computing uncertainty in providing a measure of confidence in the solution, and in designing successive measurements to improve this confidence.
Analyzing Privacy Loss in Updates of Natural Language Models
Authors: Shruti Tople, Marc Brockschmidt, Boris Köpf, Olga Ohrimenko, Santiago Zanella-Béguelin
Date: 2019-09-25 00:00:00

To continuously improve quality and reflect changes in data, machine learning-based services have to regularly re-train and update their core models. In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update.
We discuss the privacy implications of our findings, propose mitigation strategies and evaluate their effect.
Layer Flexible Adaptive Computation Time for Recurrent Neural Networks
Authors: Lida Zhang, Diego Klabjan
Date: 2019-09-25 00:00:00
Tasks: Language Modeling, Language Modelling

Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of layers, especially considering different computational needs of a sequence. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Different from the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set and Wikipedia language modeling. Experimental results show the performance improvement of 7% to 12% and indicate the model's ability to dynamically change the number of layers along with the computational steps.
Gumbel-Matrix Routing for Flexible Multi-task Learning
Authors: Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo, Luciano Sbaiz, Gabor Bartok, Jesse Berent
Date: 2019-09-25 00:00:00
Tasks: Multi-Task Learning

This paper proposes a novel per-task routing method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt performance. To address this issue, routing networks can be applied to learn to share each group of parameters with a different subset of tasks to better leverage tasks relatedness. However, this use of routing methods requires to address the challenge of learning the routing jointly with the parameters of a modular multi-task neural network. We propose the Gumbel-Matrix routing, a novel multi-task routing method based on the Gumbel-Softmax, that is designed to learn fine-grained parameter sharing. When applied to the Omniglot benchmark, the proposed method improves the state-of-the-art error rate by 17%.
OPTIMAL TRANSPORT, CYCLEGAN, AND PENALIZED LS FOR UNSUPERVISED LEARNING IN INVERSE PROBLEMS
Authors: Byeongsu Sim, Gyutaek Oh, Sungjun Lim, and Jong Chul Ye
Date: 2019-09-25 00:00:00
Tasks: Generative Adversarial Network

The penalized least squares (PLS) is a classic approach to inverse problems, where a regularization term is added to stabilize the solution. Optimal transport (OT) is another mathematical framework for computer vision tasks by providing means to transport one measure to another at minimal cost. Cycle-consistent generative adversarial network (cycleGAN) is a recent extension of GAN to learn target distributions with less mode collapsing behavior. Although similar in that no supervised training is required, the algorithms look different, so the mathematical relationship between these approaches is not clear. In this article, we provide an important advance to unveil the missing link. Specifically, we reveal that a cycleGAN architecture can be derived as  a dual formulation of the optimal transport problem, if the PLS with a  deep learning penalty is used as a transport cost between the two probability measures  from measurements  and unknown images. This suggests that cycleGAN can be considered as stochastic generalization of  classical PLS approaches. 
Our derivation is so general that various types of cycleGAN architecture can be easily derived by merely changing the transport cost. As proofs of concept, this paper provides novel cycleGAN architecture for unsupervised learning in accelerated MRI and deconvolution microscopy problems, which confirm the efficacy and the flexibility of the theory.
Why ADAM Beats SGD for Attention Models
Authors: Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J Reddi, Sanjiv Kumar, Suvrit Sra
Date: 2019-09-25 00:00:00

While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning, adaptive methods like Adam have been observed to outperform SGD across important tasks, such as attention models. The settings under which SGD performs poorly in comparison to Adam are not well understood yet. In this paper, we provide empirical and theoretical evidence that a heavy-tailed distribution of the noise in stochastic gradients is a root cause of SGD's poor performance. Based on this observation, we study clipped variants of SGD that circumvent this issue; we then analyze their convergence under heavy-tailed noise. Furthermore, we develop a new adaptive coordinate-wise clipping algorithm (ACClip) tailored to such settings. Subsequently, we show how adaptive methods like Adam can be viewed through the lens of clipping, which helps us explain Adam's strong performance under heavy-tail noise settings. Finally, we show that the proposed ACClip outperforms Adam for both BERT pretraining and finetuning tasks.
Prototype Recalls for Continual Learning
Authors: Mengmi Zhang, Tao Wang, Joo Hwee Lim, Jiashi Feng
Date: 2019-09-25 00:00:00
Tasks: Continual Learning, Metric Learning, Novel Concepts

Continual learning is a critical ability of continually acquiring and transferring knowledge without catastrophically forgetting previously learned knowledge. However, enabling continual learning for AI remains a long-standing challenge. In this work, we propose a novel method, Prototype Recalls, that efficiently embeds and recalls previously learnt knowledge to tackle catastrophic forgetting issue. In particular, we consider continual learning in classification tasks. For each classification task, our method learns a metric space containing a set of prototypes where embedding of the samples from the same class cluster around prototypes and class-representative prototypes are separated apart. To alleviate catastrophic forgetting, our method preserves the embedding function from the samples to the previous metric space, through our proposed prototype recalls from previous tasks. Specifically, the recalling process is implemented by replaying a small number of samples from previous tasks and correspondingly matching their embedding to their nearest class-representative prototypes. Compared with recent continual learning methods, our contributions are fourfold: first, our method achieves the best memory retention capability while adapting quickly to new tasks. Second, our method uses metric learning for classification and does not require adding in new neurons given new object classes. Third, our method is more memory efficient since only class-representative prototypes need to be recalled. Fourth, our method suggests a promising solution for few-shot continual learning. Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning
Authors: Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Yifeng Li
Date: 2019-09-25 00:00:00
Tasks: Anomaly Detection, Density Estimation, Disentanglement, Representation Learning, Time Series, Time Series Analysis

Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning). The disentanglement learning is currently implemented by beta-VAE for automatically discovering interpretable factorized latent representations in a completely unsupervised manner. The manifold learning is realized by t-SNE for projecting the latent representations to a 2D map.  We define a new anomaly score function by combining beta-VAE's reconstruction error in the raw feature space and local density estimation in the t-SNE space. AnoDM was evaluated on both image and time-series data and achieved better results than models that use just one of the two measures and other deep learning methods.