pwc-archive/papers-with-abstracts huggingface dataset:
Fused Gromov-Wasserstein Alignment for Hawkes Processes
Authors: Dixin Luo, Hongteng Xu, Lawrence Carin
Date: 2019-10-04 00:00:00

We propose a novel fused Gromov-Wasserstein alignment method to jointly learn the Hawkes processes in different event spaces, and align their event types. Given two Hawkes processes, we use fused Gromov-Wasserstein discrepancy to measure their dissimilarity, which considers both the Wasserstein discrepancy based on their base intensities and the Gromov-Wasserstein discrepancy based on their infectivity matrices. Accordingly, the learned optimal transport reflects the correspondence between the event types of these two Hawkes processes. The Hawkes processes and their optimal transport are learned jointly via maximum likelihood estimation, with a fused Gromov-Wasserstein regularizer. Experimental results show that the proposed method works well on synthetic and real-world data.
Adversarial Examples for Cost-Sensitive Classifiers
Authors: Gavin S. Hartnett, Andrew J. Lohn, Alexander P. Sedlack
Date: 2019-10-04 00:00:00

Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers. We use current state-of-the-art adversarially-resistant neural network classifiers [1] as the underlying models. Cost-sensitive predictions are then achieved via a final processing step in the feed-forward evaluation of the network. We evaluate the effectiveness of cost-sensitive classifiers against a variety of attacks and we introduce a new cost-sensitive attack which performs better than targeted attacks in some cases. We also explored the measures a defender can take in order to limit their vulnerability to these attacks. This attacker/defender scenario is naturally framed as a two-player zero-sum finite game which we analyze using game theory.
Stacked Wasserstein Autoencoder
Authors: Wenju Xu, Shawn Keshmiri, Guanghui Wang
Date: 2019-10-04 00:00:00
Tasks: Representation Learning

Approximating distributions over complicated manifolds, such as natural images, are conceptually attractive. The deep latent variable model, trained using variational autoencoders and generative adversarial networks, is now a key technique for representation learning. However, it is difficult to unify these two models for exact latent-variable inference and parallelize both reconstruction and sampling, partly due to the regularization under the latent variables, to match a simple explicit prior distribution. These approaches are prone to be oversimplified, and can only characterize a few modes of the true distribution. Based on the recently proposed Wasserstein autoencoder (WAE) with a new regularization as an optimal transport. The paper proposes a stacked Wasserstein autoencoder (SWAE) to learn a deep latent variable model. SWAE is a hierarchical model, which relaxes the optimal transport constraints at two stages. At the first stage, the SWAE flexibly learns a representation distribution, i.e., the encoded prior; and at the second stage, the encoded representation distribution is approximated with a latent variable model under the regularization encouraging the latent distribution to match the explicit prior. This model allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce changes in the output space. Both quantitative and qualitative results demonstrate the superior performance of SWAE compared with the state-of-the-art approaches in terms of faithful reconstruction and generation quality.
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Authors: Mathieu Seurin, Philippe Preux, Olivier Pietquin
Date: 2019-10-04 00:00:00
Tasks: Industrial Robots, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), valid

The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and Text-World domain.
Contrastive Language Adaptation for Cross-Lingual Stance Detection
Authors: Mitra Mohtarami, James Glass, Preslav Nakov
Date: 2019-10-04 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Stance Detection

We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a claim in a different target language. In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language. The evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.
Farkas layers: don't shift the data, fix the geometry
Authors: Aram-Alexandre Pooladian, Chris Finlay, Adam M. Oberman
Date: 2019-10-04 00:00:00

Successfully training deep neural networks often requires either batch normalization, appropriate weight initialization, both of which come with their own challenges. We propose an alternative, geometrically motivated method for training. Using elementary results from linear programming, we introduce Farkas layers: a method that ensures at least one neuron is active at a given layer. Focusing on residual networks with ReLU activation, we empirically demonstrate a significant improvement in training capacity in the absence of batch normalization or methods of initialization across a broad range of network sizes on benchmark datasets.
Introduction to Concentration Inequalities
Authors: Kumar Abhishek, Sneha Maheshwari, Sujit Gujar
Date: 2019-10-04 00:00:00
Tasks: BIG-bench Machine Learning

In this report, we aim to exemplify concentration inequalities and provide easy to understand proofs for it. Our focus is on the inequalities which are helpful in the design and analysis of machine learning algorithms.
Pushing the limits of RNN Compression
Authors: Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
Date: 2019-10-04 00:00:00

Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP). KPs can compress RNN layers by 16-38x with minimal accuracy loss. We show that KP can beat the task accuracy achieved by other state-of-the-art compression techniques (pruning and low-rank matrix factorization) across 4 benchmarks spanning 3 different applications, while simultaneously improving inference run-time.
BUZz: BUffer Zones for defending adversarial examples in image classification
Authors: Kaleel Mahmood, Phuong Ha Nguyen, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk
Date: 2019-10-03 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straightforward in implementation, this defense yields a unique security property which we term buffer zones. We argue that our defense based on buffer zones offers significant improvements over state-of-the-art defenses. We are able to achieve this improvement even when the adversary has access to the {\em entire} original training data set and unlimited query access to the defense. We verify our claim through experimentation using Fashion-MNIST and CIFAR-10: We demonstrate $<11\%$ attack success rate -- significantly lower than what other well-known state-of-the-art defenses offer -- at only a price of a $11-18\%$ drop in clean accuracy. By using a new intuitive metric, we explain why this trade-off offers a significant improvement over prior work.
Types for Parallel Complexity in the Pi-calculus
Authors: Patrick Baillot, Alexis Ghyselen
Date: 2019-10-03 00:00:00
Tasks: Vocal Bursts Type Prediction

Type systems as a way to control or analyze programs have been largely studied in the context of functional programming languages. Some of those work allow to extract from a typing derivation for a program a complexity bound on this program. We present how to adapt this result for parallel complexity in the pi-calculus, as a model of concurrency and parallel communication. We study two notions of time complexity: the total computation time without parallelism (the work) and the computation time under maximal parallelism (the span). We define reduction relations in the pi-calculus to capture those two notions, and we present two type systems from which one can extract a complexity bound on a process. The type systems are inspired by input/output types and size types, with temporal information about communications.
Language is Power: Representing States Using Natural Language in Reinforcement Learning
Authors: Erez Schwartz, Guy Tennenholtz, Chen Tessler, Shie Mannor
Date: 2019-10-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning methods tend to struggle. To overcome this, we explore methods for representing the semantic information embedded in the state. While previous methods focused on information in its raw form (e.g., raw visual input), we propose to represent the state using natural language. Language can represent complex scenarios and concepts, making it a favorable candidate for representation. Empirical evidence, within the domain of ViZDoom, suggests that natural language based agents are more robust, converge faster and perform better than vision based agents, showing the benefit of using natural language representations for reinforcement learning.
The asymptotic spectrum of the Hessian of DNN throughout training
Authors: Arthur Jacot, Franck Gabriel, Clément Hongler
Date: 2019-10-01 00:00:00
Proceeding: ICLR 2020 1

The dynamics of DNNs during gradient descent is described by the so-called Neural Tangent Kernel (NTK). In this article, we show that the NTK allows one to gain precise insight into the Hessian of the cost of DNNs. When the NTK is fixed during training, we obtain a full characterization of the asymptotics of the spectrum of the Hessian, at initialization and during training. In the so-called mean-field limit, where the NTK is not fixed during training, we describe the first two moments of the Hessian at initialization.
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
Authors: Cristian Bodnar, Adrian Li, Karol Hausman, Peter Pastor, Mrinal Kalakrishnan
Date: 2019-10-01 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), Robotic Grasping

The distributional perspective on reinforcement learning (RL) has given rise to a series of successful Q-learning algorithms, resulting in state-of-the-art performance in arcade game environments. However, it has not yet been analyzed how these findings from a discrete setting translate to complex practical applications characterized by noisy, high dimensional and continuous state-action spaces. In this work, we propose Quantile QT-Opt (Q2-Opt), a distributional variant of the recently introduced distributed Q-learning algorithm for continuous domains, and examine its behaviour in a series of simulated and real vision-based robotic grasping tasks. The absence of an actor in Q2-Opt allows us to directly draw a parallel to the previous discrete experiments in the literature without the additional complexities induced by an actor-critic architecture. We demonstrate that Q2-Opt achieves a superior vision-based object grasping success rate, while also being more sample efficient. The distributional formulation also allows us to experiment with various risk distortion metrics that give us an indication of how robots can concretely manage risk in practice using a Deep RL control policy. As an additional contribution, we perform batch RL experiments in our virtual environment and compare them with the latest findings from discrete settings. Surprisingly, we find that the previous batch RL findings from the literature obtained on arcade game environments do not generalise to our setup.
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets
Authors: Gokul Swamy, Siddharth Reddy, Sergey Levine, Anca D. Dragan
Date: 2019-09-22 00:00:00
Tasks: Robot Navigation

Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single human operator can manage a fleet of robots by identifying and teleoperating one robot at any given time. The key challenge is that users have limited attention: as the number of robots increases, users lose the ability to decide which robot requires teleoperation the most. Our goal is to automate this decision, thereby enabling users to supervise more robots than their attention would normally allow for. Our insight is that we can model the user's choice of which robot to control as an approximately optimal decision that maximizes the user's utility function. We learn a model of the user's preferences from observations of the user's choices in easy settings with a few robots, and use it in challenging settings with more robots to automatically identify which robot the user would most likely choose to control, if they were able to evaluate the states of all robots at all times. We run simulation experiments and a user study with twelve participants that show our method can be used to assist users in performing a simulated navigation task. We also run a hardware demonstration that illustrates how our method can be applied to a real-world mobile robot navigation task.
Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis
Authors: Vladislav Levshinskii, Maxim Polyakov, Alexander Losev, Alexander Khoperskov
Date: 2019-09-21 00:00:00
Tasks: Medical Diagnosis

The method of microwave radiometry is one of the areas of medical diagnosis of breast cancer. It is based on analysis of the spatial distribution of internal and surface tissue temperatures, which are measured in the microwave (RTM) and infrared (IR) ranges. Complex mathematical and computer models describing complex physical and biological processes within biotissue increase the efficiency of this method. Physical and biological processes are related to temperature dynamics and microwave electromagnetic radiation. Verification and validation of the numerical model is a key challenge to ensure consistency with medical big data. These data are obtained by medical measurements of patients. We present an original approach to verification and validation of simulation models of physical processes in biological tissues. Our approach is based on deep analysis of medical data and we use machine learning algorithms. We have achieved impressive success for the model of dynamics of thermal processes in a breast with cancer foci. This method allows us to carry out a significant refinement of almost all parameters of the mathematical model in order to achieve the maximum possible adequacy.
An experimental study on implicit social recommendation
Authors: Hao Ma
Date: 2013-08-01 00:00:00
Proceeding: SIGIR’13 2013 8
Tasks: Recommendation Systems

Social recommendation problems have drawn a lot of attention recently due to the prevalence of social networking
sites. The experiments in previous literature suggest that
social information is very effective in improving traditional
recommendation algorithms. However, explicit social information is not always available in most of the recommender
systems, which limits the impact of social recommendation
techniques. In this paper, we study the following two research problems: (1) In some systems without explicit social information, can we still improve recommender systems
using implicit social information? (2) In the systems with
explicit social information, can the performance of using implicit social information outperform that of using explicit social information? In order to answer these two questions, we
conduct comprehensive experimental analysis on three recommendation datasets. The result indicates that: (1) Implicit user and item social information, including similar and
dissimilar relationships, can be employed to improve traditional recommendation methods. (2) When comparing implicit social information with explicit social information, the
performance of using implicit information is slightly worse.
This study provides additional insights to social recommendation techniques, and also greatly widens the utility and
spreads the impact of previous and upcoming social recommendation approaches.
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization
Authors: Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
Date: 2019-10-04 00:00:00
Proceeding: arXiv 2019 10
Tasks: Denoising, Multiple Instance Learning, Object, Object Localization, Weakly Supervised Object Detection

In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations. The proposed approach is built on theobservation that the proposal set from the training dataset is a collectionof background, object parts, and objects. Several strategies are taken toadaptively eliminate the noisy proposals and generate pseudo object-levelannotations for the weakly labeled dataset. A multiple instance learning(MIL) algorithm enhanced by mask-out strategy is adopted to collect theclass-specific  object  proposals,  which are  then  utilized to  adapt  a pre-trained  classification  network  to  a  detection  network.  In  addition,  thedetection results from the detection network are re-weighted by jointlyconsidering the detection scores and the overlap ratio of proposals in aproposal subset optimization framework. The optimal proposals work asobject-level labels that enable a pseudo-strongly supervised dataset fortraining the detection network. Consequently, we establish a fully adap-tive detection network. Extensive evaluations on the PASCAL VOC 2007and 2012 datasets demonstrate a significant improvement compared withthe state-of-the-art methods.
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification
Authors: Hongli Wang, Jiangtao Ren
Date: 2018-11-14 00:00:00
Proceeding: Proceedings of The 10th Asian Conference on Machine Learning 2018 11
Tasks: Abstractive Text Summarization, Classification, General Classification, Sentiment Analysis, Sentiment Classification, Text Summarization

Text summarization and sentiment classification, in NLP, are two main tasks implemented
on text analysis, focusing on extracting the major idea of a text at different levels. Based
on the characteristics of both, sentiment classification can be regarded as a more abstrac-
tive summarization task. According to the scheme, a Self-Attentive Hierarchical model for
jointly improving text Summarization and Sentiment Classification (SAHSSC) is proposed
in this paper. This model jointly performs abstractive text summarization and sentiment
classification within a hierarchical end-to-end neural framework, in which the sentiment
classification layer on top of the summarization layer predicts the sentiment label in the
light of the text and the generated summary. Furthermore, a self-attention layer is also pro-
posed in the hierarchical framework, which is the bridge that connects the summarization
layer and the sentiment classification layer and aims at capturing emotional information
at text-level as well as summary-level. The proposed model can generate a more relevant
summary and lead to a more accurate summary-aware sentiment prediction. Experimental
results evaluated on SNAP amazon online review datasets show that our model outper-
forms the state-of-the-art baselines on both abstractive text summarization and sentiment
classification by a considerable margin.
Motion feature augmented network for dynamic hand gesture recognition from skeletal data
Authors: Xinghao Chen, 1 Guijin Wang, Hengkai Guo, Cairong Zhang, Hang Wang, and Li Zhang
Date: 2019-01-10 00:00:00
Proceeding: Sensors 2019 1
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Skeleton Based Action Recognition

Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC’17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC’17 dataset when compared with start-of-the-art methods.