pwc-archive/papers-with-abstracts huggingface dataset:
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
Authors: Maosen Zhang, Nan Jiang, Lei LI, Yexiang Xue
Date: 2020-11-24 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Language Modeling, Language Modelling, Sentence, Text Generation

Generating natural language under complex constraints is a principled formulation towards controllable text generation. We present a framework to allow specification of combinatorial constraints for sentence generation. We propose TSMH, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints. Our approach is highly flexible, requires no task-specific training, and leverages efficient constraint satisfaction solving techniques. To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidates that satisfy more constraints. Compared to existing MCMC approaches, our sampling approach has a better mixing performance. Experiments show that TSMH achieves consistent and significant improvement on multiple language generation tasks.
Generalized Variational Continual Learning
Authors: Noel Loo, Siddharth Swaroop, Richard E. Turner
Date: 2020-11-24 00:00:00
Proceeding: ICLR 2021 1
Tasks: Continual Learning, Variational Inference

Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL). VCL employs variational inference, which in other settings has been improved empirically by applying likelihood-tempering. We show that applying this modification to VCL recovers Online EWC as a limiting case, allowing for interpolation between the two approaches. We term the general algorithm Generalized VCL (GVCL). In order to mitigate the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, neural networks with task-specific FiLM layers, and find that this addition leads to significant performance gains, specifically for variational methods. In the small-data regime, GVCL strongly outperforms existing baselines. In larger datasets, GVCL with FiLM layers outperforms or is competitive with existing baselines in terms of accuracy, whilst also providing significantly better calibration.
Analyzing the Machine Learning Conference Review Process
Authors: David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein
Date: 2020-11-24 00:00:00
Tasks: BIG-bench Machine Learning

Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias. In this work, we critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. We quantify reproducibility/randomness in review scores and acceptance decisions, and examine whether scores correlate with paper impact. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. Furthermore, we find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts. We conclude our work with recommendations for future conference organizers.
Regret-optimal measurement-feedback control
Authors: Gautam Goel, Babak Hassibi
Date: 2020-11-24 00:00:00

We consider measurement-feedback control in linear dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing an online controller which competes with the optimal dynamic sequence of control actions selected in hindsight, instead of the best controller in some specific class of controllers. This formulation of regret is attractive when the environment changes over time and no single controller achieves good performance over the entire time horizon. We show that in the measurement-feedback setting, unlike in the full-information setting, there is no single offline controller which outperforms every other offline controller on every disturbance, and propose a new $H_2$-optimal offline controller as a benchmark for the online controller to compete against. We show that the corresponding regret-optimal online controller can be found via a novel reduction to the classical Nehari problem from robust control and present a tight data-dependent bound on its regret.
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Authors: Rui Shu, Tianpei Xia, Laurie Williams, Tim Menzies
Date: 2020-11-23 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning, Hyperparameter Optimization, Intrusion Detection

Background: Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. Goal: To help security practitioners and researchers build a more robust model against non-adaptive, white-box, and non-targeted adversarial evasion attacks through the idea of an ensemble model. Method: We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of "unexpected models"; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary's target model, with which we then make an optimized weighted ensemble prediction. Result: In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFooland Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAnd-Mal2017, and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments. Conclusion: When employing ensemble defense against adversarial evasion attacks, we suggest creating an ensemble with unexpected models that are distant from the attacker's expected model (i.e., target model) through methods such as hyperparameter optimization.
RELLIS-3D Dataset: Data, Benchmarks and Analysis
Authors: Peng Jiang, Philip Osteen, Maggie Wigness, Srikanth Saripalli
Date: 2020-11-17 00:00:00
Tasks: 3D Semantic Segmentation, Autonomous Navigation, Scene Understanding, Segmentation, Semantic Segmentation

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal dataset collected in an off-road environment, which contains annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. Additionally, we evaluate the current state-of-the-art deep learning semantic segmentation models on this dataset. Experimental results show that RELLIS-3D presents challenges for algorithms designed for segmentation in urban environments. This novel dataset provides the resources needed by researchers to continue to develop more advanced algorithms and investigate new research directions to enhance autonomous navigation in off-road environments. RELLIS-3D is available at https://github.com/unmannedlab/RELLIS-3D
A Secure Deep Probabilistic Dynamic Thermal Line Rating Prediction
Authors: N. Safari, S. M. Mazhari, C. Y. Chung, S. B. Ko
Date: 2020-11-21 00:00:00
Tasks: Prediction

Accurate short-term prediction of overhead line (OHL) transmission ampacity can directly affect the efficiency of power system operation and planning. Any overestimation of the dynamic thermal line rating (DTLR) can lead to lifetime degradation and failure of OHLs, safety hazards, etc. This paper presents a secure yet sharp probabilistic prediction model for the hour-ahead forecasting of the DTLR. The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR. The model is based on an augmented deep learning architecture that makes use of a wide range of predictors, including historical climatology data and latent variables obtained during DTLR calculation. Furthermore, by introducing a customized cost function, the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing deviations of the predicted DTLRs from the actual values. The proposed probabilistic DTLR is developed and verified using recorded experimental data. The simulation results validate the superiority of the proposed DTLR compared to state-of-the-art prediction models using well-known evaluation metrics.
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Authors: Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
Date: 2020-11-23 00:00:00
Tasks: Friction, Multi-Armed Bandits

Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to replace constants with learned contextual functions for Skype - a widely used real-time communication (RTC) application. We present Resonance, a system based on contextual bandits (CB). We describe experiences from three real-world experiments: applying it to the audio, video, and transport components in Skype. We surface a unique and practical challenge of performing machine learning (ML) inference in large software systems written using encapsulation principles. Finally, we open-source FeatureBroker, a library to reduce the friction in adopting ML models in such development environments
Human level control through deep reinforcement learning
Authors: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg1 & Demis Hassabis
Date: 2015-02-25 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?
Authors: Aditi Jha, Sumeet Agarwal
Date: 2019-03-31 00:00:00
Tasks: Human-Object Interaction Detection, Object, Scene Understanding

Visual scene understanding often requires the processing of human-object interactions. Here we seek to explore if and how well Deep Neural Network (DNN) models capture features similar to the brain's representation of humans, objects, and their interactions. We investigate brain regions which process human-, object-, or interaction-specific information, and establish correspondences between them and DNN features. Our results suggest that we can infer the selectivity of these regions to particular visual stimuli using DNN representations. We also map features from the DNN to the regions, thus linking the DNN representations to those found in specific parts of the visual cortex. In particular, our results suggest that a typical DNN representation contains encoding of compositional information for human-object interactions which goes beyond a linear combination of the encodings for the two components, thus suggesting that DNNs may be able to model this important property of biological vision.
bbw: Matching CSV to Wikidata via Meta-lookup
Authors: Renat Shigapov, Philipp Zumstein, Jan Kamlah, Lars Oberländer, Jörg Mechnich, Irene Schumm
Date: 2021-03-01 00:00:00
Tasks: Entity Typing, Graph Matching, Named Entity Recognition (NER), Relation Extraction, Table annotation

We present our publicly available semantic annotator bbw (boosted by wiki) tested at the second Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab2020). It annotates a raw CSV-table using the entities, types and properties in Wikidata. Our key ideas are meta-lookup over the SearX metasearch API and contextual matching with at least two features. Avoiding the use of dump files, we kept the storage requirements low, used only up-to-date values in Wikidata and ranked third in the challenge.
Socio-economic disparities and COVID-19 in the USA
Authors: Ayan Paul, Philipp Englert, Melinda Varga
Date: 2020-09-10 00:00:00
Tasks: Interpretable Machine Learning

COVID-19 is not a universal killer. We study the spread of COVID-19 at the county level for the United States up until the 15$^{th}$ of August, 2020. We show that the prevalence of the disease and the death rate are correlated with the local socio-economic conditions often going beyond local population density distributions, especially in rural areas. We correlate the COVID-19 prevalence and death rate with data from the US Census Bureau and point out how the spreading patterns of the disease show asymmetries in urban and rural areas separately and are preferentially affecting the counties where a large fraction of the population is non-white. Our findings can be used for more targeted policy building and deployment of resources for future occurrence of a pandemic due to SARS-CoV-2. Our methodology, based on interpretable machine learning and game theory, can be extended to study the spread of other diseases.
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
Authors: Jianrui Cai, WangMeng Zuo, and Lei Zhang
Date: 2020-05-21 00:00:00
Tasks: Deblurring, Image Deblurring

Recent years have witnessed the significant progress
on convolutional neural networks (CNNs) in dynamic scene
deblurring. While most of the CNN models are generally learned
by the reconstruction loss defined on training data, incorporating
suitable image priors as well as regularization terms into the
network architecture could boost the deblurring performance.
In this work, we propose a Dark and Bright Channel Priors
embedded Network (DBCPeNet) to plug the channel priors into
a neural network for effective dynamic scene deblurring. A
novel trainable dark and bright channel priors embedded layer
(DBCPeL) is developed to aggregate both channel priors and
blurry image representations, and a sparse regularization is
introduced to regularize the DBCPeNet model learning. Furthermore, we present an effective multi-scale network architecture,
namely image full scale exploitation (IFSE), which works in both
coarse-to-fine and fine-to-coarse manners for better exploiting
information flow across scales. Experimental results on the GoPro
and Kohler datasets show that our proposed DBCPeNet performs ¨
favorably against state-of-the-art deep image deblurring methods
in terms of both quantitative metrics and visual quality.
Utterance Retrieval Based on Recurrent Surface Text Patterns
Authors: Guillaume Dubuisson Duplessis, Franck Charras, Vincent Letard, Anne-Laure Ligozat, Sophie Rosset
Date: 2017-04-08 00:00:00
Tasks: Retrieval

This paper investigates the use of recurrent surface text patterns to represent and index open-domain dialogue utterances for a retrieval system that can be embedded in a conversational agent. This approach involves both the building of a database of such patterns by mining a corpus of written dialogic interactions, and the exploitation of this database in a generalised vector space model for utterance retrieval. It is a corpus-based, unsupervised, parameterless and language-independent process. Our study indicates that the proposed model performs objectively well comparatively to other retrieval models on a task of selection of dialogue examples derived from a large corpus of written dialogues.
An Open Framework for Remote-PPG Methods and their Assessment
Authors: Giuseppe Boccignone, Donatello Conte, Vittorio Cuculo, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti
Date: 2020-11-26 00:00:00
Tasks: Heart rate estimation, Photoplethysmography (PPG), Physiological Computing, POS

This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped. The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following conditions should be met: i) a structured pipeline to monitor rPPG algorithms’ input, output, and main control parameters; ii) the availability and the use of multiple datasets; iii) a sound statistical assessment of methods’ performance. The proposed framework is instantiated in the form of a Python package named pyVHR (short for Python tool for Virtual Heart Rate), which is made freely available on GitHub (github.com/phuselab/pyVHR). Here, to substantiate our approach, we evaluate eight well-known rPPG methods, through extensive experiments across five public video datasets, and subsequent nonparametric statistical analysis. Surprisingly, performances achieved by the four best methods, namely POS, CHROM, PCA and SSR, are not significantly different from a statistical standpoint highlighting the importance of evaluate the different approaches with a statistical assessment.
CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair
Authors: Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, Lin Tan
Date: 2020-07-18 00:00:00
Tasks: Ensemble Learning, Machine Translation, NMT, Program Repair, Translation

Automated generate-and-validate (GV) program repair techniques (APR) typically rely on hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a significant amount of manual effort to discover and it is hard to adapt these rules to different programming languages.

To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages. To better represent the context of a bug, we introduce a new context-aware NMT architecture that represents the buggy source code and its surrounding context separately. CoCoNuT uses CNNs instead of recurrent neural networks (RNNs), since CNN layers can be stacked to extract hierarchical features and better model source code at different granularity levels (e.g., statements and functions). In addition, CoCoNuT takes advantage of the randomness in hyperparameter tuning to build multiple models that fix different bugs and combines these models using ensemble learning to fix more bugs.

Our evaluation on six popular benchmarks for four programming languages (Java, C, Python, and JavaScript) shows that CoCoNuT correctly fixes (i.e., the first generated patch is semantically equivalent to the developer’s patch) 509 bugs, including 309 bugs that are fixed by none of the 27 techniques with which we compare.
HMMs for Unsupervised Vietnamese WordSegmentation
Authors: Ba-Long Bui, Thi-Trang Nguyen, Huu-Hoang Nguyen, Kiem-Hieu Nguyen
Date: 2019-05-16 00:00:00
Tasks: Segmentation

Word segmentation is an important problem in nat-ural language processing. Most of previous works on Vietnameseword segmentation are supervised learning. In this paper, wepropose an unsupervised method for Vietnamese word segmenta-tion based on Hidden Markov Models. We naturally encode priorlinguistic knowledge into model learning. In decoding, we proposean enhancement of Viterbi decoding algorithm with externaltoken ordering statistics from Pointwise Mutual Information.Evaluation on benchmark datasets shows that the proposedmethod works reasonably well.
Sourcecode is available at https://github.com/longbb/wordrecognition