pwc-archive/papers-with-abstracts huggingface dataset:
Neural Rough Differential Equations for Long Time Series
Authors: James Morrill, Cristopher Salvi, Patrick Kidger, James Foster, Terry Lyons
Date: 2020-09-17 00:00:00
Tasks: Irregular Time Series, Time Series, Time Series Analysis, Time Series Classification

Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series. Existing methods for computing the forward pass of a Neural CDE involve embedding the incoming time series into path space, often via interpolation, and using evaluations of this path to drive the hidden state. Here, we use rough path theory to extend this formulation. Instead of directly embedding into path space, we instead represent the input signal over small time intervals through its \textit{log-signature}, which are statistics describing how the signal drives a CDE. This is the approach for solving \textit{rough differential equations} (RDEs), and correspondingly we describe our main contribution as the introduction of Neural RDEs. This extension has a purpose: by generalising the Neural CDE approach to a broader class of driving signals, we demonstrate particular advantages for tackling long time series. In this regime, we demonstrate efficacy on problems of length up to 17k observations and observe significant training speed-ups, improvements in model performance, and reduced memory requirements compared to existing approaches.
Learning to Identify Physical Parameters from Video Using Differentiable Physics
Authors: Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Jörg Stückler
Date: 2020-09-17 00:00:00
Tasks: Friction, Predict Future Video Frames, Representation Learning, Self-Supervised Learning, Video Prediction

Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep learning based architectures typically lack a physically interpretable latent space. In this study, we use a differentiable physics engine within an action-conditional video representation network to learn a physical latent representation. We propose supervised and self-supervised learning methods to train our network and identify physical properties. The latter uses spatial transformers to decode physical states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. In experiments we demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences in the simulated scenarios. We evaluate the accuracy of our supervised and self-supervised methods and compare it with a system identification baseline which directly learns from state trajectories. We also demonstrate the ability of our method to predict future video frames from input images and actions.
A Linked Aggregate Code for Processing Faces (Revised Version)
Authors: Michael Lyons, Kazunori Morikawa
Date: 2020-09-17 00:00:00

A model of face representation, inspired by the biology of the visual system, is compared to experimental data on the perception of facial similarity. The face representation model uses aggregate primary visual cortex (V1) cell responses topographically linked to a grid covering the face, allowing comparison of shape and texture at corresponding points in two facial images. When a set of relatively similar faces was used as stimuli, this Linked Aggregate Code (LAC) predicted human performance in similarity judgment experiments. When faces of perceivable categories were used, dimensions such as apparent sex and race emerged from the LAC model without training. The dimensional structure of the LAC similarity measure for the mixed category task displayed some psychologically plausible features but also highlighted differences between the model and the human similarity judgements. The human judgements exhibited a racial perceptual bias that was not shared by the LAC model. The results suggest that the LAC based similarity measure may offer a fertile starting point for further modelling studies of face representation in higher visual areas, including studies of the development of biases in face perception.
Video based real-time positional tracker
Authors: David Albarracín, Jesús Hormigo, José David Fernández
Date: 2020-09-17 00:00:00
Tasks: Position

We propose a system that uses video as the input to track the position of objects relative to their surrounding environment in real-time. The neural network employed is trained on a 100% synthetic dataset coming from our own automated generator. The positional tracker relies on a range of 1 to n video cameras placed around an arena of choice. The system returns the positions of the tracked objects relative to the broader world by understanding the overlapping matrices formed by the cameras and therefore these can be extrapolated into real world coordinates. In most cases, we achieve a higher update rate and positioning precision than any of the existing GPS-based systems, in particular for indoor objects or those occluded from clear sky.
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals
Authors: Saloni Dash, Vineeth N Balasubramanian, Amit Sharma
Date: 2020-09-17 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual, Fairness, valid

Counterfactual examples for an input -- perturbations that change specific features but not others -- have been shown to be useful for evaluating bias of machine learning models, e.g., against specific demographic groups. However, generating counterfactual examples for images is non-trivial due to the underlying causal structure on the various features of an image. To be meaningful, generated perturbations need to satisfy constraints implied by the causal model. We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI), that generates counterfactuals in accordance with the causal relationships between attributes of an image. Based on the generated counterfactuals, we show how to explain a pre-trained machine learning classifier, evaluate its bias, and mitigate the bias using a counterfactual regularizer. On the Morpho-MNIST dataset, our method generates counterfactuals comparable in quality to prior work on SCM-based counterfactuals (DeepSCM), while on the more complex CelebA dataset our method outperforms DeepSCM in generating high-quality valid counterfactuals. Moreover, generated counterfactuals are indistinguishable from reconstructed images in a human evaluation experiment and we subsequently use them to evaluate the fairness of a standard classifier trained on CelebA data. We show that the classifier is biased w.r.t. skin and hair color, and how counterfactual regularization can remove those biases.
Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling
Authors: Timothy Rumbell, Jaimit Parikh, James Kozloski, Viatcheslav Gurev
Date: 2020-09-17 00:00:00
Tasks: Bayesian Inference, parameter estimation

Predictions for physical systems often rely upon knowledge acquired from ensembles of entities, e.g., ensembles of cells in biological sciences. For qualitative and quantitative analysis, these ensembles are simulated with parametric families of mechanistic models (MM). Two classes of methodologies, based on Bayesian inference and Population of Models, currently prevail in parameter estimation for physical systems. However, in Bayesian analysis, uninformative priors for MM parameters introduce undesirable bias. Here, we propose how to infer parameters within the framework of stochastic inverse problems (SIP), also termed data-consistent inversion, wherein the prior targets only uncertainties that arise due to MM non-invertibility. To demonstrate, we introduce new methods to solve SIP based on rejection sampling, Markov chain Monte Carlo, and generative adversarial networks (GANs). In addition, to overcome limitations of SIP, we reformulate SIP based on constrained optimization and present a novel GAN to solve the constrained optimization problem.
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Authors: Wenhao Li, Ningyuan Chen, L. Jeff Hong
Date: 2020-09-17 00:00:00
Tasks: Dimensionality Reduction, Variable Selection

We consider a contextual online learning (multi-armed bandit) problem with high-dimensional covariate $\mathbf{x}$ and decision $\mathbf{y}$. The reward function to learn, $f(\mathbf{x},\mathbf{y})$, does not have a particular parametric form. The literature has shown that the optimal regret is $\tilde{O}(T^{(d_x+d_y+1)/(d_x+d_y+2)})$, where $d_x$ and $d_y$ are the dimensions of $\mathbf x$ and $\mathbf y$, and thus it suffers from the curse of dimensionality. In many applications, only a small subset of variables in the covariate affect the value of $f$, which is referred to as \textit{sparsity} in statistics. To take advantage of the sparsity structure of the covariate, we propose a variable selection algorithm called \textit{BV-LASSO}, which incorporates novel ideas such as binning and voting to apply LASSO to nonparametric settings. Our algorithm achieves the regret $\tilde{O}(T^{(d_x^*+d_y+1)/(d_x^*+d_y+2)})$, where $d_x^*$ is the effective covariate dimension. The regret matches the optimal regret when the covariate is $d^*_x$-dimensional and thus cannot be improved. Our algorithm may serve as a general recipe to achieve dimension reduction via variable selection in nonparametric settings.
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA
Authors: Ieva Staliūnaitė, Ignacio Iacobacci
Date: 2020-09-17 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Conversational Question Answering, Negation, Question Answering, Semantic Role Labeling, Word Embeddings

Many NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This paper studies the types of linguistic phenomena accounted for by language models in the context of a Conversational Question Answering (CoQA) task. We identify the problematic areas for the finetuned RoBERTa, BERT and DistilBERT models through systematic error analysis - basic arithmetic (counting phrases), compositional semantics (negation and Semantic Role Labeling), and lexical semantics (surprisal and antonymy). When enhanced with the relevant linguistic knowledge through multitask learning, the models improve in performance. Ensembles of the enhanced models yield a boost between 2.2 and 2.7 points in F1 score overall, and up to 42.1 points in F1 on the hardest question classes. The results show differences in ability to represent compositional and lexical information between RoBERTa, BERT and DistilBERT.
Adversarial Image Composition with Auxiliary Illumination
Authors: Fangneng Zhan, Shijian Lu, Changgong Zhang, Feiying Ma, Xuansong Xie
Date: 2020-09-17 00:00:00

Dealing with the inconsistency between a foreground object and a background image is a challenging task in high-fidelity image composition. State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected. In this paper, we propose an Adversarial Image Composition Net (AIC-Net) that achieves realistic image composition by considering potential shadows that the foreground object projects in the composed image. A novel branched generation mechanism is proposed, which disentangles the generation of shadows and the transfer of foreground styles for optimal accomplishment of the two tasks simultaneously. A differentiable spatial transformation module is designed which bridges the local harmonization and the global harmonization to achieve their joint optimization effectively. Extensive experiments on pedestrian and car composition tasks show that the proposed AIC-Net achieves superior composition performance qualitatively and quantitatively.
Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection
Authors: Sumesh Thakur, Jiju Peethambaran
Date: 2020-09-17 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Graph Neural Network, Object, object-detection, Object Detection

A robust and accurate 3D detection system is an integral part of autonomous vehicles. Traditionally, a majority of 3D object detection algorithms focus on processing 3D point clouds using voxel grids or bird's eye view (BEV). Recent works, however, demonstrate the utilization of the graph neural network (GNN) as a promising approach to 3D object detection. In this work, we propose an attention based feature aggregation technique in GNN for detecting objects in LiDAR scan. We first employ a distance-aware down-sampling scheme that not only enhances the algorithmic performance but also retains maximum geometric features of objects even if they lie far from the sensor. In each layer of the GNN, apart from the linear transformation which maps the per node input features to the corresponding higher level features, a per node masked attention by specifying different weights to different nodes in its first ring neighborhood is also performed. The masked attention implicitly accounts for the underlying neighborhood graph structure of every node and also eliminates the need of costly matrix operations thereby improving the detection accuracy without compromising the performance. The experiments on KITTI dataset show that our method yields comparable results for 3D object detection.
Learning a Deep Part-based Representation by Preserving Data Distribution
Authors: Anyong Qin, Zhaowei Shang, Zhuolin Tan, Taiping Zhang, Yuan Yan Tang
Date: 2020-09-17 00:00:00
Tasks: Density Estimation, Dimensionality Reduction, Network Embedding

Unsupervised dimensionality reduction is one of the commonly used techniques in the field of high dimensional data recognition problems. The deep autoencoder network which constrains the weights to be non-negative, can learn a low dimensional part-based representation of data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass samples. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. In this paper, by preserving the data distribution, a deep part-based representation can be learned, and the novel algorithm is called Distribution Preserving Network Embedding (DPNE). In DPNE, we first need to estimate the distribution of the original high dimensional data using the $k$-nearest neighbor kernel density estimation, and then we seek a part-based representation which respects the above distribution. The experimental results on the real-world data sets show that the proposed algorithm has good performance in terms of cluster accuracy and AMI. It turns out that the manifold structure in the raw data can be well preserved in the low dimensional feature space.
What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus
Authors: Yonatan Bilu, Shai Gretz, Edo Cohen, Noam Slonim
Date: 2020-09-17 00:00:00
Tasks: Benchmarking, Term Extraction

One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia. In this work we ask what makes a term worthy of entering this edifice of knowledge, and having a page of its own in Wikipedia? To what extent is this a natural product of on-going human discourse and discussion rather than an idiosyncratic choice of Wikipedia editors? Specifically, we aim to identify such "wiki-worthy" terms in a massive news corpus, and see if this can be done with no, or minimal, dependency on actual Wikipedia entries. We suggest a five-step pipeline for doing so, providing baseline results for all five, and the relevant datasets for benchmarking them. Our work sheds new light on the domain-specific Automatic Term Extraction problem, with the problem at hand being a domain-independent variant of it.
Label Smoothing and Adversarial Robustness
Authors: Chaohao Fu, Hongbin Chen, Na Ruan, Weijia Jia
Date: 2020-09-17 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness

Recent studies indicate that current adversarial attack methods are flawed and easy to fail when encountering some deliberately designed defense. Sometimes even a slight modification in the model details will invalidate the attack. We find that training model with label smoothing can easily achieve striking accuracy under most gradient-based attacks. For instance, the robust accuracy of a WideResNet model trained with label smoothing on CIFAR-10 achieves 75% at most under PGD attack. To understand the reason underlying the subtle robustness, we investigate the relationship between label smoothing and adversarial robustness. Through theoretical analysis about the characteristics of the network trained with label smoothing and experiment verification of its performance under various attacks. We demonstrate that the robustness produced by label smoothing is incomplete based on the fact that its defense effect is volatile, and it cannot defend attacks transferred from a naturally trained model. Our study enlightens the research community to rethink how to evaluate the model's robustness appropriately.
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Authors: Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Date: 2020-09-17 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Chunking, Variational Inference

The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forward-backward and Viterbi algorithms are typically applied in training and prediction stages of the CRF model. However, these algorithms require sequential computation that makes parallelization impossible. In this paper, we propose to employ a parallelizable approximate variational inference algorithm for the CRF model. Based on this algorithm, we design an approximate inference network that can be connected with the encoder of the neural CRF model to form an end-to-end network, which is amenable to parallelization for faster training and prediction. The empirical results show that our proposed approaches achieve a 12.7-fold improvement in decoding speed with long sentences and a competitive accuracy compared with the traditional CRF approach.
LeadCache: Regret-Optimal Caching in Networks
Authors: Debjit Paria, Abhishek Sinha
Date: 2020-09-17 00:00:00
Tasks: Combinatorial Optimization

We consider an online prediction problem in the context of network caching. Assume that multiple users are connected to several caches via a bipartite network. At any time slot, each user may request an arbitrary file chosen from a large catalog. A user's request at a slot is met if the requested file is cached in at least one of the caches connected to the user. Our objective is to predict, prefetch, and optimally distribute the files on the caches at each slot to maximize the total number of cache hits. The problem is non-trivial due to the non-convex and non-smooth nature of the objective function. In this paper, we propose $\texttt{LeadCache}$ - an efficient online caching policy based on the Follow-the-Perturbed-Leader paradigm. We show that $\texttt{LeadCache}$ is regret-optimal up to a factor of $\tilde{O}(n^{3/8}),$ where $n$ is the number of users. We design two efficient implementations of the $\texttt{LeadCache}$ policy, one based on Pipage rounding and the other based on Madow's sampling, each of which makes precisely one call to an LP-solver per iteration. Furthermore, with a Strong-Law-type assumption, we show that the total number of file fetches under $\texttt{LeadCache}$ remains almost surely finite over an infinite horizon. Finally, we derive an approximately tight regret lower bound using results from graph coloring. We conclude that the learning-based $\texttt{LeadCache}$ policy decisively outperforms the state-of-the-art caching policies both theoretically and empirically.
Deep Learning Approaches to Classification of Production Technology for 19th Century Books
Authors: Chanjong Im, Junaid Ghauri, John Rothman, Thomas Mandl
Date: 2020-09-17 00:00:00
Tasks: Classification, General Classification

Cultural research is dedicated to understanding the processes of knowledge dissemination and the social and technological practices in the book industry. Research on children books in the 19th century can be supported by computer systems. Specifically, the advances in digital image processing seem to offer great opportunities for analyzing and quantifying the visual components in the books. The production technology for illustrations in books in the 19th century was characterized by a shift from wood or copper engraving to lithography. We report classification experiments which intend to classify images based on the production technology. For a classification task that is also difficult for humans, the classification quality reaches only around 70%. We analyze some further error sources and identify reasons for the low performance.
Learning to Personalize for Web Search Sessions
Authors: Saad Aloteibi, Stephen Clark
Date: 2020-09-17 00:00:00
Tasks: Learning-To-Rank, Session Search

The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a personalization task under the framework of learning to rank. Personalization approaches re-rank results to match a user model. Such user models are usually accumulated over time based on the user's browsing behaviour. We use a pre-computed and transparent set of user models based on concepts from the social science literature. Interaction data are used to map each session to these user models. Novel features are then estimated based on such models as well as sessions' interaction data. Extensive experiments on test collections from the TREC session track show statistically significant improvements over current session search algorithms.