pwc-archive/papers-with-abstracts huggingface dataset:
Demystifying and Generalizing BinaryConnect
Authors: Tim Dockhorn, YaoLiang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
Date: 2021-10-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Quantization

BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite limited. We attempt to close this gap in four different aspects: (a) we show that existing quantization algorithms, including post-training quantization, are surprisingly similar to each other; (b) we argue for proximal maps as a natural family of quantizers that is both easy to design and analyze; (c) we refine the observation that BC is a special case of dual averaging, which itself is a special case of the generalized conditional gradient algorithm; (d) consequently, we propose ProxConnect (PC) as a generalization of BC and we prove its convergence properties by exploiting the established connections. We conduct experiments on CIFAR-10 and ImageNet, and verify that PC achieves competitive performance.
A Probabilistic Framework for Knowledge Graph Data Augmentation
Authors: Jatin Chauhan, Priyanshu Gupta, Pasquale Minervini
Date: 2021-10-25 00:00:00
Tasks: Data Augmentation, Knowledge Graph Completion, Link Prediction

We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the learning process of neural link predictors. Our method can generate potentially diverse triples with the advantage of being efficient and scalable as well as agnostic to the choice of the link prediction model and dataset used. Experiments and analysis done on popular models and benchmarks show that NNMFAug can bring notable improvements over the baselines.
Nested Graph Neural Networks
Authors: Muhan Zhang, Pan Li
Date: 2021-10-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Graph Classification, Graph Neural Network, Graph Property Prediction

Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a center node, both 1-WL and GNN obtain a node representation that encodes a rooted subtree around the center node. These rooted subtree representations are then pooled into a single representation to represent the whole graph. However, rooted subtrees are of limited expressiveness to represent a non-tree graph. To address it, we propose Nested Graph Neural Networks (NGNNs). NGNN represents a graph with rooted subgraphs instead of rooted subtrees, so that two graphs sharing many identical subgraphs (rather than subtrees) tend to have similar representations. The key is to make each node representation encode a subgraph around it more than a subtree. To achieve this, NGNN extracts a local subgraph around each node and applies a base GNN to each subgraph to learn a subgraph representation. The whole-graph representation is then obtained by pooling these subgraph representations. We provide a rigorous theoretical analysis showing that NGNN is strictly more powerful than 1-WL. In particular, we proved that NGNN can discriminate almost all r-regular graphs, where 1-WL always fails. Moreover, unlike other more powerful GNNs, NGNN only introduces a constant-factor higher time complexity than standard GNNs. NGNN is a plug-and-play framework that can be combined with various base GNNs. We test NGNN with different base GNNs on several benchmark datasets. NGNN uniformly improves their performance and shows highly competitive performance on all datasets.
Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning
Authors: Nick Lorenzo, Sean O'Rourke, Theresa Scarnati
Date: 2021-10-25 00:00:00
Tasks: Transfer Learning

In order to encode additional statistical information in data fusion and transfer learning applications, we introduce a generalized covariance constraint for the matching component analysis (MCA) transfer learning technique. We provide a closed-form solution to the resulting covariance-generalized optimization problem and an algorithm for its computation. We call the resulting technique -- applicable to both data fusion and transfer learning -- covariance-generalized MCA (CGMCA). We also demonstrate via numerical experiments that CGMCA is capable of meaningfully encoding into its maps more information than MCA.
Iterative Rule Extension for Logic Analysis of Data: an MILP-based heuristic to derive interpretable binary classification from large datasets
Authors: Marleen Balvert
Date: 2021-10-25 00:00:00
Tasks: Binary Classification, Decision Making, Sensitivity, Specificity

Data-driven decision making is rapidly gaining popularity, fueled by the ever-increasing amounts of available data and encouraged by the development of models that can identify beyond linear input-output relationships. Simultaneously the need for interpretable prediction- and classification methods is increasing, as this improves both our trust in these models and the amount of information we can abstract from data. An important aspect of this interpretability is to obtain insight in the sensitivity-specificity trade-off constituted by multiple plausible input-output relationships. These are often shown in a receiver operating characteristic (ROC) curve. These developments combined lead to the need for a method that can abstract complex yet interpretable input-output relationships from large data, i.e. data containing large numbers of samples and sample features. Boolean phrases in disjunctive normal form (DNF) are highly suitable for explaining non-linear input-output relationships in a comprehensible way. Mixed integer linear programming (MILP) can be used to abstract these Boolean phrases from binary data, though its computational complexity prohibits the analysis of large datasets. This work presents IRELAND, an algorithm that allows for abstracting Boolean phrases in DNF from data with up to 10,000 samples and sample characteristics. The results show that for large datasets IRELAND outperforms the current state-of-the-art and can find solutions for datasets where current models run out of memory or need excessive runtimes. Additionally, by construction IRELAND allows for an efficient computation of the sensitivity-specificity trade-off curve, allowing for further understanding of the underlying input-output relationship.
Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis
Authors: Jing Lin, Yu Zhang, Edwin Khoo
Date: 2021-10-25 00:00:00
Tasks: Prognosis

Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation. Compared to existing battery modeling efforts, we aim to build a model with physics as its backbone and statistical learning techniques as enhancements. Such a hybrid model has better generalizability and interpretability together with a well-calibrated uncertainty associated with its prediction, rendering it more valuable and relevant to safety-critical applications under realistic usage scenarios.
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Authors: Zixiang Chen, Dongruo Zhou, Quanquan Gu
Date: 2021-10-25 00:00:00
Proceeding: NeurIPS 2021 12

Escaping from saddle points and finding local minimum is a central problem in nonconvex optimization. Perturbed gradient methods are perhaps the simplest approach for this problem. However, to find $(\epsilon, \sqrt{\epsilon})$-approximate local minima, the existing best stochastic gradient complexity for this type of algorithms is $\tilde O(\epsilon^{-3.5})$, which is not optimal. In this paper, we propose LENA (Last stEp shriNkAge), a faster perturbed stochastic gradient framework for finding local minima. We show that LENA with stochastic gradient estimators such as SARAH/SPIDER and STORM can find $(\epsilon, \epsilon_{H})$-approximate local minima within $\tilde O(\epsilon^{-3} + \epsilon_{H}^{-6})$ stochastic gradient evaluations (or $\tilde O(\epsilon^{-3})$ when $\epsilon_H = \sqrt{\epsilon}$). The core idea of our framework is a step-size shrinkage scheme to control the average movement of the iterates, which leads to faster convergence to the local minima.
An algorithm for the computation of joint Hawkes moments with exponential kernel
Authors: Nicolas Privault
Date: 2021-10-25 00:00:00

The purpose of this paper is to present a recursive algorithm and its implementation in Maple and Mathematica for the computation of joint moments and cumulants of Hawkes processes with exponential kernels. Numerical results and computation times are also discussed. Obtaining closed form expressions can be computationally intensive, as joint fifth cumulant and moment formulas can be respectively expanded into up to 3,288 and 27,116 summands.
Contrastive Neural Processes for Self-Supervised Learning
Authors: Konstantinos Kallidromitis, Denis Gudovskiy, Kazuki Kozuka, Iku Ohama, Luca Rigazio
Date: 2021-10-24 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Self-Supervised Learning, Time Series, Time Series Analysis, Time Series Forecasting

Recent contrastive methods show significant improvement in self-supervised learning in several domains. In particular, contrastive methods are most effective where data augmentation can be easily constructed e.g. in computer vision. However, they are less successful in domains without established data transformations such as time series data. In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes. It relies on recent advances in neural processes to perform time series forecasting. This allows to generate augmented versions of data by employing a set of various sampling functions and, hence, avoid manually designed augmentations. We extend conventional neural processes and propose a new contrastive loss to learn times series representations in a self-supervised setup. Therefore, unlike previous self-supervised methods, our augmentation pipeline is task-agnostic, enabling our method to perform well across various applications. In particular, a ResNet with a linear classifier trained using our approach is able to outperform state-of-the-art techniques across industrial, medical and audio datasets improving accuracy over 10% in ECG periodic data. We further demonstrate that our self-supervised representations are more efficient in the latent space, improving multiple clustering indexes and that fine-tuning our method on 10% of labels achieves results competitive to fully-supervised learning.
DAG Card is the new Model Card
Authors: Jacopo Tagliabue, Ville Tuulos, Ciro Greco, Valay Dave
Date: 2021-10-24 00:00:00
Tasks: model

With the progressive commoditization of modeling capabilities, data-centric AI recognizes that what happens before and after training becomes crucial for real-world deployments. Following the intuition behind Model Cards, we propose DAG Cards as a form of documentation encompassing the tenets of a data-centric point of view. We argue that Machine Learning pipelines (rather than models) are the most appropriate level of documentation for many practical use cases, and we share with the community an open implementation to generate cards from code.
Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling
Authors: Shangeth Rajaa, Pham Van Tung, Chng Eng Siong
Date: 2021-10-24 00:00:00
Tasks: Age Estimation, Recommendation Systems, Representation Learning, Speaker Profiling

Speaker profiling, which aims to estimate speaker characteristics such as age and height, has a wide range of applications inforensics, recommendation systems, etc. In this work, we propose a semisupervised learning approach to mitigate the issue of low training data for speaker profiling. This is done by utilizing external corpus with speaker information to train a better representation which can help to improve the speaker profiling systems. Specifically, besides the standard supervised learning path, the proposed framework has two more paths: (1) an unsupervised speaker representation learning path that helps to capture the speaker information; (2) a consistency training path that helps to improve the robustness of the system by enforcing it to produce similar predictions for utterances of the same speaker.The proposed approach is evaluated on the TIMIT and NISP datasets for age, height, and gender estimation, while the Librispeech is used as the unsupervised external corpus. Trained both on single-task and multi-task settings, our approach was able to achieve state-of-the-art results on age estimation on the TIMIT Test dataset with Root Mean Square Error(RMSE) of6.8 and 7.4 years and Mean Absolute Error(MAE) of 4.8 and5.0 years for male and female speakers respectively.
A deep learning based surrogate model for stochastic simulators
Authors: Akshay Thakur, Souvik Chakraborty
Date: 2021-10-24 00:00:00
Tasks: Deep Learning

We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network to approximate the stochastic response. The challenge with such a framework resides in designing the network architecture and selecting loss-function suitable for stochastic response. While we utilize a simple feed-forward neural network, we propose to use conditional maximum mean discrepancy (CMMD) as the loss-function. CMMD exploits the property of reproducing kernel Hilbert space and allows capturing discrepancy between the between the target and the neural network predicted distributions. The proposed approach is mathematically rigorous, in the sense that it makes no assumptions about the probability density function of the response. Performance of the proposed approach is illustrated using four benchmark problems selected from the literature. Results obtained indicate the excellent performance of the proposed approach.
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Xiangzhan Yu, Furu Wei
Date: 2021-10-26 00:00:00
Tasks: Denoising, Self-Supervised Learning, Speech Denoising, speech-recognition, Speech Recognition

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.
Assessing Evaluation Metrics for Speech-to-Speech Translation
Authors: Elizabeth Salesky, Julian Mäder, Severin Klinger
Date: 2021-10-26 00:00:00
Tasks: Machine Translation, Open-Ended Question Answering, Speech Synthesis, Speech-to-Speech Translation, Translation

Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speech-to-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.
Annotating Implicit Reasoning in Arguments with Causal Links
Authors: Keshav Singh, Naoya Inoue, Farjana Sultana Mim, Shoichi Naitoh, Kentaro Inui
Date: 2021-10-26 00:00:00

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge. However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise). In this work, we focus on identifying the implicit knowledge in the form of argumentation knowledge which can help in understanding the reasoning link in arguments. Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge that explicates the implicit reasoning in arguments via causality. We create a novel two-phase annotation process with simplified guidelines and show how to collect and filter high-quality implicit reasonings via crowdsourcing. We find substantial inter-annotator agreement for quality evaluation between experts, but find evidence that casts a few questions on the feasibility of collecting high-quality semi-structured implicit reasoning through our crowdsourcing process. We release our materials(i.e., crowdsourcing guidelines and collected implicit reasonings) to facilitate further research towards the structured representation of argumentation knowledge.
s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning
Authors: Hangbo Bao, Li Dong, Wenhui Wang, Nan Yang, Furu Wei
Date: 2021-10-26 00:00:00
Tasks: Abstractive Text Summarization, Question Generation, Question-Generation, Text Generation

Pretrained bidirectional Transformers, such as BERT, have achieved significant improvements in a wide variety of language understanding tasks, while it is not straightforward to directly apply them for natural language generation. In this paper, we present a sequence-to-sequence fine-tuning toolkit s2s-ft, which adopts pretrained Transformers for conditional generation tasks. Inspired by UniLM, we implement three sequence-to-sequence fine-tuning algorithms, namely, causal fine-tuning, masked fine-tuning, and pseudo-masked fine-tuning. By leveraging the existing pretrained bidirectional Transformers, experimental results show that s2s-ft achieves strong performance on several benchmarks of abstractive summarization, and question generation. Moreover, we demonstrate that the package s2s-ft supports both monolingual and multilingual NLG tasks. The s2s-ft toolkit is available at https://github.com/microsoft/unilm/tree/master/s2s-ft.
Open Rule Induction
Authors: Wanyun Cui, Xingran Chen
Date: 2021-10-26 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Language Modelling, Relation Extraction

Rules have a number of desirable properties. It is easy to understand, infer new knowledge, and communicate with other inference systems. One weakness of the previous rule induction systems is that they only find rules within a knowledge base (KB) and therefore cannot generalize to more open and complex real-world rules. Recently, the language model (LM)-based rule generation are proposed to enhance the expressive power of the rules. In this paper, we revisit the differences between KB-based rule induction and LM-based rule generation. We argue that, while KB-based methods inducted rules by discovering data commonalities, the current LM-based methods are "learning rules from rules". This limits these methods to only produce "canned" rules whose patterns are constrained by the annotated rules, while discarding the rich expressive power of LMs for free text. Therefore, in this paper, we propose the open rule induction problem, which aims to induce open rules utilizing the knowledge in LMs. Besides, we propose the Orion (\underline{o}pen \underline{r}ule \underline{i}nducti\underline{on}) system to automatically mine open rules from LMs without supervision of annotated rules. We conducted extensive experiments to verify the quality and quantity of the inducted open rules. Surprisingly, when applying the open rules in downstream tasks (i.e. relation extraction), these automatically inducted rules even outperformed the manually annotated rules.
Part & Whole Extraction: Towards A Deep Understanding of Quantitative Facts for Percentages in Text
Authors: Lei Fang, Jian-Guang Lou
Date: 2021-10-26 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence

We study the problem of quantitative facts extraction for text with percentages. For example, given the sentence "30 percent of Americans like watching football, while 20% prefer to watch NBA.", our goal is to obtain a deep understanding of the percentage numbers ("30 percent" and "20%") by extracting their quantitative facts: part ("like watching football" and "prefer to watch NBA") and whole ("Americans). These quantitative facts can empower new applications like automated infographic generation. We formulate part and whole extraction as a sequence tagging problem. Due to the large gap between part/whole and its corresponding percentage, we introduce skip mechanism in sequence modeling, and achieved improved performance on both our task and the CoNLL-2003 named entity recognition task. Experimental results demonstrate that learning to skip in sequence tagging is promising.
Assessing the Sufficiency of Arguments through Conclusion Generation
Authors: Timon Gurcke, Milad Alshomary, Henning Wachsmuth
Date: 2021-10-26 00:00:00
Proceeding: EMNLP (ArgMining) 2021 11
Tasks: text-classification, Text Classification

The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of the individual premises, and similar. An argument whose premises make its conclusion rationally worthy to be drawn is called sufficient in argument quality research. Previous work tackled sufficiency assessment as a standard text classification problem, not modeling the inherent relation of premises and conclusion. In this paper, we hypothesize that the conclusion of a sufficient argument can be generated from its premises. To study this hypothesis, we explore the potential of assessing sufficiency based on the output of large-scale pre-trained language models. Our best model variant achieves an F1-score of .885, outperforming the previous state-of-the-art and being on par with human experts. While manual evaluation reveals the quality of the generated conclusions, their impact remains low ultimately.