pwc-archive/papers-with-abstracts huggingface dataset:
The Mathematical Structure of Integrated Information Theory
Authors: Johannes Kleiner, Sean Tull
Date: 2020-02-18 00:00:00

Integrated Information Theory is one of the leading models of consciousness. It aims to describe both the quality and quantity of the conscious experience of a physical system, such as the brain, in a particular state. In this contribution, we propound the mathematical structure of the theory, separating the essentials from auxiliary formal tools. We provide a definition of a generalized IIT which has IIT 3.0 of Tononi et. al., as well as the Quantum IIT introduced by Zanardi et. al. as special cases. This provides an axiomatic definition of the theory which may serve as the starting point for future formal investigations and as an introduction suitable for researchers with a formal background.
Uncertainty Estimation in Autoregressive Structured Prediction
Authors: Andrey Malinin, Mark Gales
Date: 2020-02-18 00:00:00
Proceeding: ICLR 2021 1
Tasks: Image Classification, Machine Translation, Prediction, speech-recognition, Speech Recognition, Structured Prediction, Translation

Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for autoregressive structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT'14 English-French and WMT'17 English-German translation and LibriSpeech speech recognition datasets.
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting
Authors: Hanwei Wu, Ather Gattami, Markus Flierl
Date: 2020-02-18 00:00:00
Tasks: Representation Learning, Time Series, Time Series Analysis, Time Series Forecasting

We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets. Direct trend classification using deep neural networks trained on small datasets is susceptible to the overfitting problem. In this paper, we propose to first learn compact representations from time series data, then use the learned representations to train a simpler model for predicting time series movements. We consider a class-conditioned latent variable model. We train an encoder network to maximize the mutual information between the latent variables and the trend information conditioned on the encoded observed variables. We show that conditional mutual information maximization can be approximated by a contrastive loss. Then, the problem is transformed into a classification task of determining whether two encoded representations are sampled from the same class or not. This is equivalent to performing pairwise comparisons of the training datapoints, and thus, improves the generalization ability of the encoder network. We use deep autoregressive models as our encoder to capture long-term dependencies of the sequence data. Empirical experiments indicate that our proposed method has the potential to advance state-of-the-art performance.
Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Authors: T. Tony Cai, Hongzhe Li, Rong Ma
Date: 2020-02-18 00:00:00
Tasks: Clustering

Driven by a wide range of applications, many principal subspace estimation problems have been studied individually under different structural constraints. This paper presents a unified framework for the statistical analysis of a general structured principal subspace estimation problem which includes as special cases non-negative PCA/SVD, sparse PCA/SVD, subspace constrained PCA/SVD, and spectral clustering. General minimax lower and upper bounds are established to characterize the interplay between the information-geometric complexity of the structural set for the principal subspaces, the signal-to-noise ratio (SNR), and the dimensionality. The results yield interesting phase transition phenomena concerning the rates of convergence as a function of the SNRs and the fundamental limit for consistent estimation. Applying the general results to the specific settings yields the minimax rates of convergence for those problems, including the previous unknown optimal rates for non-negative PCA/SVD, sparse SVD and subspace constrained PCA/SVD.
Text Classification with Lexicon from PreAttention Mechanism
Authors: QINGBIAO LI, ChunHua Wu, KangFeng Zheng
Date: 2020-02-18 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. And it improves the utilization of the linguistic knowledge. Although it is helpful for the task, the lexicon has got little attention in recent neural network models. Firstly, getting a high-quality lexicon is not easy. We lack an effective automated lexicon extraction method, and most lexicons are hand crafted, which is very inefficient for big data. What's more, there is no an effective way to use a lexicon in a neural network. To address those limitations, we propose a Pre-Attention mechanism for text classification in this paper, which can learn attention of different words according to their effects in the classification tasks. The words with different attention can form a domain lexicon. Experiments on three benchmark text classification tasks show that our models get competitive result comparing with the state-of-the-art methods. We get 90.5% accuracy on Stanford Large Movie Review dataset, 82.3% on Subjectivity dataset, 93.7% on Movie Reviews. And compared with the text classification model without Pre-Attention mechanism, those with Pre-Attention mechanism improve by 0.9%-2.4% accuracy, which proves the validity of the Pre-Attention mechanism. In addition, the Pre-Attention mechanism performs well followed by different types of neural networks (e.g., convolutional neural networks and Long Short-Term Memory networks). For the same dataset, when we use Pre-Attention mechanism to get attention value followed by different neural networks, those words with high attention values have a high degree of coincidence, which proves the versatility and portability of the Pre-Attention mechanism. we can get stable lexicons by attention values, which is an inspiring method of information extraction.
AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting
Authors: Shaolong Sun, Dongchuan Yang, Ju-e Guo, Shouyang Wang
Date: 2020-02-18 00:00:00

Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an efficient and robust forecasting approach due to the inherent randomness and variations of metro passenger flow. In this study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows, and it combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network. The AdaEnsemble learning approach consists of three important stages. The first stage applies VMD to decompose the metro passenger flows data into periodic component, deterministic component and volatility component. Then we employ SARIMA model to forecast the periodic component, LSTM network to learn and forecast deterministic component and MLP network to forecast volatility component. In the last stage, the diverse forecasted components are reconstructed by another MLP network. The empirical results show that our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system and several standard evaluation measures.
A Model to Measure the Spread Power of Rumors
Authors: Zoleikha Jahanbakhsh-Nagadeh, Mohammad-Reza Feizi-Derakhshi, Majid Ramezani, Taymaz Akan, Meysam Asgari-Chenaghlu, Narjes Nikzad-Khasmakhi, Ali-Reza Feizi-Derakhshi, Mehrdad Ranjbar-Khadivi, Elnaz Zafarani-Moattar, Mohammad-Ali Balafar
Date: 2020-02-18 00:00:00
Tasks: Rumour Detection

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors.
Hierarchical Transformer Network for Utterance-level Emotion Recognition
Authors: QingBiao Li, CHUNHUA WU, KangFeng Zheng, Zhe Wang
Date: 2020-02-18 00:00:00
Tasks: Emotion Recognition in Conversation, text-classification

While there have been significant advances in de-tecting emotions in text, in the field of utter-ance-level emotion recognition (ULER), there are still many problems to be solved. In this paper, we address some challenges in ULER in dialog sys-tems. (1) The same utterance can deliver different emotions when it is in different contexts or from different speakers. (2) Long-range contextual in-formation is hard to effectively capture. (3) Unlike the traditional text classification problem, this task is supported by a limited number of datasets, among which most contain inadequate conversa-tions or speech. To address these problems, we propose a hierarchical transformer framework (apart from the description of other studies, the "transformer" in this paper usually refers to the encoder part of the transformer) with a lower-level transformer to model the word-level input and an upper-level transformer to capture the context of utterance-level embeddings. We use a pretrained language model bidirectional encoder representa-tions from transformers (BERT) as the lower-level transformer, which is equivalent to introducing external data into the model and solve the problem of data shortage to some extent. In addition, we add speaker embeddings to the model for the first time, which enables our model to capture the in-teraction between speakers. Experiments on three dialog emotion datasets, Friends, EmotionPush, and EmoryNLP, demonstrate that our proposed hierarchical transformer network models achieve 1.98%, 2.83%, and 3.94% improvement, respec-tively, over the state-of-the-art methods on each dataset in terms of macro-F1.
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots
Authors: Neelesh Kumar, Konstantinos P. Michmizos
Date: 2020-02-18 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

Although cognitive engagement (CE) is crucial for motor learning, it remains underutilized in rehabilitation robots, partly because its assessment currently relies on subjective and gross measurements taken intermittently. Here, we propose an end-to-end computational framework that assesses CE in real-time, using electroencephalography (EEG) signals as objective measurements. The framework consists of i) a deep convolutional neural network (CNN) that extracts task-discriminative spatiotemporal EEG to predict the level of CE for two classes -- cognitively engaged vs. disengaged; and ii) a novel sliding window method that predicts continuous levels of CE in real-time. We evaluated our framework on 8 subjects using an in-house Go/No-Go experiment that adapted its gameplay parameters to induce cognitive fatigue. The proposed CNN had an average leave-one-out accuracy of 88.13\%. The CE prediction correlated well with a commonly used behavioral metric based on self-reports taken every 5 minutes ($\rho$=0.93). Our results objectify CE in real-time and pave the way for using CE as a rehabilitation parameter for tailoring robotic therapy to each patient's needs and skills.
A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head
Authors: Praveenram Balachandar, Konstantinos P. Michmizos
Date: 2020-02-18 00:00:00

Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient assumptions, such as static camera systems and deep learning algorithms devouring high volumes of ideally slightly-varying data are hard to survive. Leveraging on recent studies on the neural connectome associated with eye movements, we designed a neuromorphic oculomotor controller and placed it at the heart of our in-house biomimetic robotic head prototype. The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' topology, the SNN is biologically interpretable and requires no training to operate. Here, we report the robot's target tracking ability, demonstrate that its eye kinematics are similar to those reported in human eye studies and show that a biologically-constrained learning, although not required for the SNN's function, can be used to further refine its performance. This work aligns with our ongoing effort to develop energy-efficient neuromorphic SNNs and harness their emerging intelligence to control biomimetic robots with versatility and robustness.
Improved Optimistic Algorithms for Logistic Bandits
Authors: Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq
Date: 2020-02-18 00:00:00
Proceeding: ICML 2020 1

The generalized linear bandit framework has attracted a lot of attention in recent years by extending the well-understood linear setting and allowing to model richer reward structures. It notably covers the logistic model, widely used when rewards are binary. For logistic bandits, the frequentist regret guarantees of existing algorithms are $\tilde{\mathcal{O}}(\kappa \sqrt{T})$, where $\kappa$ is a problem-dependent constant. Unfortunately, $\kappa$ can be arbitrarily large as it scales exponentially with the size of the decision set. This may lead to significantly loose regret bounds and poor empirical performance. In this work, we study the logistic bandit with a focus on the prohibitive dependencies introduced by $\kappa$. We propose a new optimistic algorithm based on a finer examination of the non-linearities of the reward function. We show that it enjoys a $\tilde{\mathcal{O}}(\sqrt{T})$ regret with no dependency in $\kappa$, but for a second order term. Our analysis is based on a new tail-inequality for self-normalized martingales, of independent interest.
A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups
Authors: Piotr Kicki, Mete Ozay, Piotr Skrzypczyński
Date: 2020-02-18 00:00:00
Tasks: Efficient Neural Network

We introduce a method to design a computationally efficient $G$-invariant neural network that approximates functions invariant to the action of a given permutation subgroup $G \leq S_n$ of the symmetric group on input data. The key element of the proposed network architecture is a new $G$-invariant transformation module, which produces a $G$-invariant latent representation of the input data. This latent representation is then processed with a multi-layer perceptron in the network. We prove the universality of the proposed architecture, discuss its properties and highlight its computational and memory efficiency. Theoretical considerations are supported by numerical experiments involving different network configurations, which demonstrate the effectiveness and strong generalization properties of the proposed method in comparison to other $G$-invariant neural networks.
A Survey of Deep Learning Techniques for Neural Machine Translation
Authors: Shuoheng Yang, Yuxin Wang, Xiaowen Chu
Date: 2020-02-18 00:00:00
Tasks: Deep Learning, Machine Translation, NMT, Survey, Translation

In recent years, natural language processing (NLP) has got great development with deep learning techniques. In the sub-field of machine translation, a new approach named Neural Machine Translation (NMT) has emerged and got massive attention from both academia and industry. However, with a significant number of researches proposed in the past several years, there is little work in investigating the development process of this new technology trend. This literature survey traces back the origin and principal development timeline of NMT, investigates the important branches, categorizes different research orientations, and discusses some future research trends in this field.
Few-Shot Few-Shot Learning and the role of Spatial Attention
Authors: Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
Date: 2020-02-18 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning.
Gradient $\ell_1$ Regularization for Quantization Robustness
Authors: Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling
Date: 2020-02-18 00:00:00
Proceeding: ICLR 2020 1
Tasks: Quantization

We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized on-demand to different bit-widths as energy and memory requirements of the application change. Unlike quantization-aware training using the straight-through estimator that only targets a specific bit-width and requires access to training data and pipeline, our regularization-based method paves the way for "on the fly'' post-training quantization to various bit-widths. We show that by modeling quantization as a $\ell_\infty$-bounded perturbation, the first-order term in the loss expansion can be regularized using the $\ell_1$-norm of gradients. We experimentally validate the effectiveness of our regularization scheme on different architectures on CIFAR-10 and ImageNet datasets.
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
Authors: Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng
Date: 2020-02-18 00:00:00
Tasks: General Classification, Node Classification

Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on designing more effective models without considering much about the quality of the input data. In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification. As graph data consist of both topology and node labels, we improve input data quality from both perspectives. For topology, we observe that higher classification accuracy can be achieved when the ratio of inter-class edges (connecting nodes from different classes) is low and propose topology update to remove inter-class edges and add intra-class edges. For node labels, we propose training node augmentation, which enlarges the training set using the labels predicted by existing GNN models. SEG is a general framework that can be easily combined with existing GNN models. Experimental results validate that SEG consistently improves the performance of well-known GNN models such as GCN, GAT and SGC across different datasets.
Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders
Authors: Andrea Asperti, Matteo Trentin
Date: 2020-02-18 00:00:00

In the loss function of Variational Autoencoders there is a well known tension between two components: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space. Correctly balancing these two components is a delicate issue, easily resulting in poor generative behaviours. In a recent work, Dai and Wipf obtained a sensible improvement by allowing the network to learn the balancing factor during training, according to a suitable loss function. In this article, we show that learning can be replaced by a simple deterministic computation, helping to understand the underlying mechanism, and resulting in a faster and more accurate behaviour. On typical datasets such as Cifar and Celeba, our technique sensibly outperforms all previous VAE architectures.