pwc-archive/papers-with-abstracts huggingface dataset:
Domain Invariant Representation Learning with Domain Density Transformations
Authors: A. Tuan Nguyen, Toan Tran, Yarin Gal, Atılım Güneş Baydin
Date: 2021-02-09 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Domain Generalization, Representation Learning

Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to target domains. To tackle this problem, a predominant approach is to find and learn some domain-invariant information in order to use it for the prediction task. In this paper, we propose a theoretically grounded method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains. We also show how to use generative adversarial networks to learn such domain transformations to implement our method in practice. We demonstrate the effectiveness of our method on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
Emotion Transfer Using Vector-Valued Infinite Task Learning
Authors: Alex Lambert, Sanjeel Parekh, Zoltán Szabó, Florence d'Alché-Buc
Date: 2021-02-09 00:00:00
Tasks: BIG-bench Machine Learning, Emotion Classification, Style Transfer

Style transfer is a significant problem of machine learning with numerous successful applications. In this work, we present a novel style transfer framework building upon infinite task learning and vector-valued reproducing kernel Hilbert spaces. We instantiate the idea in emotion transfer where the goal is to transform facial images to different target emotions. The proposed approach provides a principled way to gain explicit control over the continuous style space. We demonstrate the efficiency of the technique on popular facial emotion benchmarks, achieving low reconstruction cost and high emotion classification accuracy.
Point Cloud Transformers applied to Collider Physics
Authors: Vinicius Mikuni, Florencia Canelli
Date: 2021-02-09 00:00:00
Tasks: BIG-bench Machine Learning, Jet Tagging

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.
The Role of the Input in Natural Language Video Description
Authors: Silvia Cascianelli, Gabriele Costante, Alessandro Devo, Thomas A. Ciarfuglia, Paolo Valigi, Mario L. Fravolini
Date: 2021-02-09 00:00:00
Tasks: Data Augmentation, Video Description

Natural Language Video Description (NLVD) has recently received strong interest in the Computer Vision, Natural Language Processing (NLP), Multimedia, and Autonomous Robotics communities. The State-of-the-Art (SotA) approaches obtained remarkable results when tested on the benchmark datasets. However, those approaches poorly generalize to new datasets. In addition, none of the existing works focus on the processing of the input to the NLVD systems, which is both visual and textual. In this work, it is presented an extensive study dealing with the role of the visual input, evaluated with respect to the overall NLP performance. This is achieved performing data augmentation of the visual component, applying common transformations to model camera distortions, noise, lighting, and camera positioning, that are typical in real-world operative scenarios. A t-SNE based analysis is proposed to evaluate the effects of the considered transformations on the overall visual data distribution. For this study, it is considered the English subset of Microsoft Research Video Description (MSVD) dataset, which is used commonly for NLVD. It was observed that this dataset contains a relevant amount of syntactic and semantic errors. These errors have been amended manually, and the new version of the dataset (called MSVD-v2) is used in the experimentation. The MSVD-v2 dataset is released to help to gain insight into the NLVD problem.
Bounded Memory Active Learning through Enriched Queries
Authors: Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz
Date: 2021-02-09 00:00:00
Tasks: Active Learning

The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select informative examples in order to lower prohibitively expensive labeling costs. Unfortunately, in standard worst-case models of learning, the active setting often provides no improvement over non-adaptive algorithms. To combat this, a series of recent works have considered a model in which the learner may ask enriched queries beyond labels. While such models have seen success in drastically lowering label costs, they tend to come at the expense of requiring large amounts of memory. In this work, we study what families of classifiers can be learned in bounded memory. To this end, we introduce a novel streaming-variant of enriched-query active learning along with a natural combinatorial parameter called lossless sample compression that is sufficient for learning not only with bounded memory, but in a query-optimal and computationally efficient manner as well. Finally, we give three fundamental examples of classifier families with small, easy to compute lossless compression schemes when given access to basic enriched queries: axis-aligned rectangles, decision trees, and halfspaces in two dimensions.
On the Robustness of Multi-View Rotation Averaging
Authors: Xinyi Li, Haibin Ling
Date: 2021-02-09 00:00:00

Rotation averaging is a synchronization process on single or multiple rotation groups, and is a fundamental problem in many computer vision tasks such as multi-view structure from motion (SfM). Specifically, rotation averaging involves the recovery of an underlying pose-graph consistency from pairwise relative camera poses. Specifically, given pairwise motion in rotation groups, especially 3-dimensional rotation groups (\eg, $\mathbb{SO}(3)$), one is interested in recovering the original signal of multiple rotations with respect to a fixed frame. In this paper, we propose a robust framework to solve multiple rotation averaging problem, especially in the cases that a significant amount of noisy measurements are present. By introducing the $\epsilon$-cycle consistency term into the solver, we enable the robust initialization scheme to be implemented into the IRLS solver. Instead of conducting the costly edge removal, we implicitly constrain the negative effect of erroneous measurements by weight reducing, such that IRLS failures caused by poor initialization can be effectively avoided. Experiment results demonstrate that our proposed approach outperforms state of the arts on various benchmarks.
Student sentiment Analysis Using Classification With Feature Extraction Techniques
Authors: Latika Tamrakar, Dr. Padmavati Shrivastava, Dr. S. M. Ghosh
Date: 2021-02-01 00:00:00
Tasks: Classification, General Classification

Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Web-based learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficial to learning if it must be used effectively. In this paper, we worked on how machine learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) can be applied over Web-based learning, emphasis given on sentiment present in the feedback students. We also work on two types of Feature Extraction Technique (FETs) namely Count Vector (CVr) or Bag of Words) (BoW) and Term Frequency and Inverse Document Frequency (TF-IDF) Vector. In the research study, it is our goal for our proposed LR, SVM, NB, and DT models to classify the presence of Student Feedback Dataset (SFB) with improved accuracy with cleaned dataset and feature extraction techniques. The SFB is one of the significant concerns among the student sentimental analysis.
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers
Authors: Leo Laugier, John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon
Date: 2021-02-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Denoising, Self-Supervised Learning, Style Transfer, Text Style Transfer

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.
Heterogeneous Choice Sets and Preferences
Authors: Levon Barseghyan, Maura Coughlin, Francesca Molinari, Joshua C. Teitelbaum
Date: 2019-07-04 00:00:00

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non-singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher-dimensional unobserved heterogeneity.
Equilibrium Behaviors in Repeated Games
Authors: Yingkai Li, Harry Pei
Date: 2020-07-28 00:00:00

We examine a patient player's behavior when he can build reputations in front of a sequence of myopic opponents. With positive probability, the patient player is a commitment type who plays his Stackelberg action in every period. We characterize the patient player's action frequencies in equilibrium. Our results clarify the extent to which reputations can refine the patient player's behavior and provide new insights to entry deterrence, business transactions, and capital taxation. Our proof makes a methodological contribution by establishing a new concentration inequality.
Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems
Authors: Xianrui Zheng, YuLan Liu, Deniz Gunceler, Daniel Willett
Date: 2020-11-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Today, many state-of-the-art automatic speech recognition (ASR) systems apply all-neural models that map audio to word sequences trained end-to-end along one global optimisation criterion in a fully data driven fashion. These models allow high precision ASR for domains and words represented in the training material but have difficulties recognising words that are rarely or not at all represented during training, i.e. trending words and new named entities. In this paper, we use a text-to-speech (TTS) engine to provide synthetic audio for out-of-vocabulary (OOV) words. We aim to boost the recognition accuracy of a recurrent neural network transducer (RNN-T) on OOV words by using the extra audio-text pairs, while maintaining the performance on the non-OOV words. Different regularisation techniques are explored and the best performance is achieved by fine-tuning the RNN-T on both original training data and extra synthetic data with elastic weight consolidation (EWC) applied on the encoder. This yields a 57% relative word error rate (WER) reduction on utterances containing OOV words without any degradation on the whole test set.
Interactive Network Visualization of Opioid Crisis Related Data- Policy, Pharmaceutical, Training, and More
Authors: Olga Scrivner, Elizabeth McAvoy, Thuy Nguyen, Tenzin Choeden, Kosali Simon, Katy Börner
Date: 2021-02-10 00:00:00
Tasks: Decision Making

Responding to the U.S. opioid crisis requires a holistic approach supported by evidence from linking and analyzing multiple data sources. This paper discusses how 20 available resources can be combined to answer pressing public health questions related to the crisis. It presents a network view based on U.S. geographical units and other standard concepts, crosswalked to communicate the coverage and interlinkage of these resources. These opioid-related datasets can be grouped by four themes: (1) drug prescriptions, (2) opioid related harms, (3) opioid treatment workforce, jobs, and training, and (4) drug policy. An interactive network visualization was created and is freely available online; it lets users explore key metadata, relevant scholarly works, and data interlinkages in support of informed decision making through data analysis.
No-Reference Image Quality Assessment with Global Statistical Features
Authors: Domonkos Varga
Date: 2021-02-05 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

The perceptual quality of digital images is often deteriorated during storage, compression, and transmission. The most reliable way of assessing image quality is to ask people to provide their opinions on a number of test images. However, this is an expensive and time-consuming process which cannot be applied in real-time systems. In this study, a novel no-reference image quality assessment method is proposed. The introduced method uses a set of novel quality-aware features which globally characterizes the statistics of a given test image, such as extended local fractal dimension distribution feature, extended first digit distribution features using different domains, Bilaplacian features, image moments, and a wide variety of perceptual features. Experimental results are demonstrated on five publicly available benchmark image quality assessment databases: CSIQ, MDID, KADID-10k, LIVE In the Wild, and KonIQ-10k.
Color Image Demosaicking Using a 3-Stage Convolutional Neural Network Structure
Authors: Kai Cui, Zhi Jin, Eckehard Steinbach
Date: 2018-10-07 00:00:00
Tasks: Demosaicking

Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras. Conventional CDM approaches are mostly based on interpolation schemes and hand-crafted image priors, which result in unpleasant visual artifacts in some cases. Motivated by the special characteristics of inter-channel correlations (higher correlations for R/G and G/B channels than that for R/B), in this paper, a 3-stage convolutional neural network (CNN) structure for CDM is proposed. In the first stage, the G channel is reconstructed independently. Then, by using the reconstructed G channel as guidance, the R and B channels are recovered in the second stage. Finally, high-quality RGB color images are reconstructed in the third stage. The objective and visual quality evaluation results show that the proposed structure achieves noticeable quality improvements in comparison to the state-of-the-art approaches.
A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting
Authors: Francis X. Diebold, Maximilian Gobel
Date: 2021-01-25 00:00:00

We propose a reduced-form benchmark predictive model (BPM) for fixed-target forecasting of Arctic sea ice extent, and we provide a case study of its real-time performance for target date September 2020. We visually detail the evolution of the statistically-optimal point, interval, and density forecasts as time passes, new information arrives, and the end of September approaches. Comparison to the BPM may prove useful for evaluating and selecting among various more sophisticated dynamical sea ice models, which are widely used to quantify the likely future evolution of Arctic conditions and their two-way interaction with economic activity.
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming
Date: 2021-02-08 00:00:00

One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution of a fixed bitrate ladder is inadequate in ensuring a high quality of user experience without re-buffering or introducing annoying compression artifacts. However, a content-tailored solution, based on extensively encoding across all resolutions and over a wide quality range is highly expensive in terms of computational, financial, and energy costs. Inspired by this, we propose an approach that exploits machine learning to predict a content-optimized bitrate ladder. The method extracts spatio-temporal features from the uncompressed content, trains machine-learning models to predict the Pareto front parameters, and, based on that, builds the ladder within a defined bitrate range. The method has the benefit of significantly reducing the number of encodes required per sequence. The presented results, based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of encodes required when compared to an exhaustive search and an interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of an average Bj{\o}ntegaard Delta Rate difference of 1.78% compared to the exhaustive approach. Finally, a hybrid method is introduced that selects either the proposed or the interpolation-based method depending on the sequence features. This results in an overall 83.83% reduction of required encodings at the cost of an average Bj{\o}ntegaard Delta Rate difference of 1.26%.
Machine vision for low-cost remote control of mosquitoes by power laser
Authors: Ildar Rakhmatulin
Date: 2021-02-11 00:00:00

In this paper, we present an innovative and effective method for remote monitoring of mosquitoes and their neutralization. We explain in detail how we leverage modern advances in neural networks to use a powerful laser to neutralize mosquitoes. The paper presented the experimental low-cost prototype for mosquito control, which uses a powerful laser to thermally neutralize the mosquitoes. The developed device is controlled by a single-board computer based on the neural network. The paper demonstrated experimental research for mosquito neutralization during which, to maximize approximation to natural conditions, simulation of various working conditions was conducted. The manuscript showed that a low-cost device can be used to kill mosquitoes with a powerful laser.
Hybrid Neural Fusion for Full-frame Video Stabilization
Authors: Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
Date: 2021-02-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: Optical Flow Estimation, Video Stabilization

Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we present a frame synthesis algorithm to achieve full-frame video stabilization. We first estimate dense warp fields from neighboring frames and then synthesize the stabilized frame by fusing the warped contents. Our core technical novelty lies in the learning-based hybrid-space fusion that alleviates artifacts caused by optical flow inaccuracy and fast-moving objects. We validate the effectiveness of our method on the NUS, selfie, and DeepStab video datasets. Extensive experiment results demonstrate the merits of our approach over prior video stabilization methods.
Disentangled Representations from Non-Disentangled Models
Authors: Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets, Artem Babenko
Date: 2021-02-11 00:00:00
Tasks: Disentanglement, Fairness

Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is currently to train a generative model that separates different factors of variation in its latent space. This separation is typically enforced by training with specific regularization terms in the model's objective function. These terms, however, introduce additional hyperparameters responsible for the trade-off between disentanglement and generation quality. While tuning these hyperparameters is crucial for proper disentanglement, it is often unclear how to tune them without external supervision. This paper investigates an alternative route to disentangled representations. Namely, we propose to extract such representations from the state-of-the-art generative models trained without disentangling terms in their objectives. This paradigm of post hoc disentanglement employs little or no hyperparameters when learning representations while achieving results on par with existing state-of-the-art, as shown by comparison in terms of established disentanglement metrics, fairness, and the abstract reasoning task. All our code and models are publicly available.
Proof Artifact Co-training for Theorem Proving with Language Models
Authors: Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu
Date: 2021-02-11 00:00:00
Proceeding: ICLR 2022 4
Tasks: Automated Theorem Proving, Imitation Learning, Language Modeling, Language Modelling

Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime. We propose PACT ({\bf P}roof {\bf A}rtifact {\bf C}o-{\bf T}raining), a general methodology for extracting abundant self-supervised data from kernel-level proof terms for co-training alongside the usual tactic prediction objective. We apply this methodology to Lean, an interactive proof assistant which hosts some of the most sophisticated formalized mathematics to date. We instrument Lean with a neural theorem prover driven by a Transformer language model and show that PACT improves theorem proving success rate on a held-out suite of test theorems from 32\% to 48\%.