pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Road Boundary for Intelligent Vehicles Based on DeepLabV3+ Architecture
Authors: Sunanda Das, Awal Ahmed Fime, Nazmul Siddique, M. M. A. Hashem
Date: 2021-08-24 00:00:00
Proceeding: IEEE Access ( Volume: 9) 2021 8
Tasks: Autonomous Vehicles, Decoder, Semantic Segmentation

Road boundary estimation is an essential task for autonomous vehicles and intelligent driving assistants. It is considerably straightforward to attain the task when roads are marked properly with indicators. However, estimating road boundary reliably without prior knowledge of the road, such as road markings, is extremely difficult. This paper proposes a method to estimate road boundaries in different environments with deep learning-based semantic segmentation, and without any predefined road markings. The proposed method employed an encoder-decoder-based DeepLab architecture for segmentation with different types of backbone networks such as VGG16, VGG19, ResNet-50, and ResNet-101 while handling the class imbalance problem by weighing the loss contribution of the model’s different outputs. The performance of the proposed method is verified using the ‘ICCV09DATA’ dataset. The method outperformed other existing methods and achieved the accuracy, precision, recall, f-measure of 0.9596±0.0097, 0.9453±0.0118, 0.9369±0.0149, and 0.9408±0.0135 respectively while using RestNet-101 as a backbone network and Dice Coefficient as a loss function. The detailed experimental analysis confirms the feasibility of the proposed method for road boundary estimation in different challenging environments.
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
Authors: Jie Li, Yongli Ren, Ke Deng
Date: 2022-04-25 00:00:00
Proceeding: Proceedings of the ACM Web Conference 2022 4
Tasks: Exposure Fairness, Fairness, Recommendation Systems

Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that require careful consideration in online platforms. Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN  mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. FairGAN does not explicitly treat unobserved interactions as negative, but instead, adopts a novel fairness-aware learning strategy to dynamically generate fairness signals. This optimizes the search direction to make FairGAN capable of searching the space of the optimal ranking that can fairly allocate exposure to individual items while preserving users’ utilities as high as possible.
The Surfer Model with a Hybrid Approach to Ranking the Web Pages
Authors: Javad Paksima  Department of Engineering, Payame Noor Yazd University, Iran  Paksima@pnu.ac.ir Homa Khajeh*  Department of Engineering, Science and Art University, Yazd, Iran  khajeh121@yahoo.com
Date: 2016-07-26 00:00:00
Proceeding: journal 2016 7
Tasks: Information Retrieval, Retrieval

Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages
must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information
retrieval, it is highly important to design a ranking algorithm to provide the results pertaining to user’s query due to the
great deal of information on the World Wide Web. In this paper, a ranking method is proposed with a hybrid approach,
which considers the content and connections of pages. The proposed model is a smart surfer that passes or hops from the
current page to one of the externally linked pages with respect to their content. A probability, which is obtained using the
learning automata along with content and links to pages, is used to select a webpage to hop. For a transition to another
page, the content of pages linked to it are used. As the surfer moves about the pages, the PageRank score of a page is
recursively calculated. Two standard datasets named TD2003 and TD2004 were used to evaluate and investigate the
proposed method. They are the subsets of dataset LETOR3. The results indicated the superior performance of the
proposed approach over other methods introduced in this area.
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks
Authors: Sumin Lee, Myeonghun Lee, Ki-Won Gyak, Sung Dug Kim, Mi-Jeong Kim, Kyoungmin Min
Date: 2022-04-04 00:00:00
Proceeding: ACS Omega 2022 4

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.
Learning cosmology and clustering with cosmic graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Date: 2022-04-28 00:00:00
Tasks: Clustering

We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to work with irregular and sparse data, like the distribution of galaxies in the Universe. We first show that GNNs can learn to compute the power spectrum of galaxy catalogues with a few percent accuracy. We then train GNNs to perform likelihood-free inference at the galaxy-field level. Our models are able to infer the value of $\Omega_{\rm m}$ with a $\sim12\%-13\%$ accuracy just from the positions of $\sim1000$ galaxies in a volume of $(25~h^{-1}{\rm Mpc})^3$ at $z=0$ while accounting for astrophysical uncertainties as modelled in CAMELS. Incorporating information from galaxy properties, such as stellar mass, stellar metallicity, and stellar radius, increases the accuracy to $4\%-8\%$. Our models are built to be translational and rotational invariant, and they can extract information from any scale larger than the minimum distance between two galaxies. However, our models are not completely robust: testing on simulations run with a different subgrid physics than the ones used for training does not yield as accurate results.
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
Authors: Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou
Date: 2022-04-29 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: 4k, Conversational Question Answering, Question Answering, Spoken Language Understanding, Transfer Learning

In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows given the speech documents. In this task, our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering. To this end, instead of directly adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which effectively ingests cross-modal information to achieve fine-grained representations of the speech and language modalities. Moreover, we propose a simple and novel mechanism, termed Dual Attention, by encouraging better alignments between audio and text to ease the process of knowledge transfer. To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations. The performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of cross-modal information integration. Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering tasks.
How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?
Authors: Shiyue Zhang, Vishrav Chaudhary, Naman Goyal, James Cross, Guillaume Wenzek, Mohit Bansal, Francisco Guzman
Date: 2022-04-29 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, Translation

A multilingual tokenizer is a fundamental component of multilingual neural machine translation. It is trained from a multilingual corpus. Since a skewed data distribution is considered to be harmful, a sampling strategy is usually used to balance languages in the corpus. However, few works have systematically answered how language imbalance in tokenizer training affects downstream performance. In this work, we analyze how translation performance changes as the data ratios among languages vary in the tokenizer training corpus. We find that while relatively better performance is often observed when languages are more equally sampled, the downstream performance is more robust to language imbalance than we usually expected. Two features, UNK rate and closeness to the character level, can warn of poor downstream performance before performing the task. We also distinguish language sampling for tokenizer training from sampling for model training and show that the model is more sensitive to the latter.
Finite Entailment of UCRPQs over ALC Ontologies
Authors: Vıctor Gutiérrez-Basulto, Albert Gutowski, Yazmın Ibáñez-Garcıa, Filip Murlak
Date: 2022-04-29 00:00:00

We investigate the problem of finite entailment of ontology-mediated queries. We consider the expressive query language, unions of conjunctive regular path queries (UCRPQs), extending the well-known class of union of conjunctive queries, with regular expressions over roles. We look at ontologies formulated using the description logic ALC, and show a tight 2EXPTIME upper bound for entailment of UCRPQs. At the core of our decision procedure, there is a novel automata-based technique introducing a stratification of interpretations induced by the deterministic finite automaton underlying the input UCRPQ
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation
Authors: Gusseppe Bravo-Rocca, Peini Liu, Jordi Guitart, Ajay Dholakia, David Ellison, Miroslav Hodak
Date: 2022-04-29 00:00:00
Tasks: Data Augmentation, Transfer Learning

Increasing a ML model accuracy is not enough, we must also increase its trustworthiness. This is an important step for building resilient AI systems for safety-critical applications such as automotive, finance, and healthcare. For that purpose, we propose a multi-agent system that combines both machine and human agents. In this system, a checker agent calculates a trust score of each instance (which penalizes overconfidence and overcautiousness in predictions) using an agreement-based method and ranks it; then an improver agent filters the anomalous instances based on a human rule-based procedure (which is considered safe), gets the human labels, applies geometric data augmentation, and retrains with the augmented data using transfer learning. We evaluate the system on corrupted versions of the MNIST and FashionMNIST datasets. We get an improvement in accuracy and trust score with just few additional labels compared to a baseline approach.
Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems
Authors: Jakub Kudela, Radomil Matousek
Date: 2022-04-29 00:00:00
Tasks: Evolutionary Algorithms

Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many real-world optimization problems, these evaluations involve computationally expensive numerical simulations or physical experiments. Surrogate-assisted evolutionary algorithms (SAEAs) have recently gained increased attention for their performance in solving these types of problems. The main idea of SAEAs is the integration of an evolutionary algorithm with a selected surrogate model that approximates the computationally expensive function. In this paper, we propose a surrogate model based on a Lipschitz underestimation and use it to develop a differential evolution-based algorithm. The algorithm, called Lipschitz Surrogate-assisted Differential Evolution (LSADE), utilizes the Lipschitz-based surrogate model, along with a standard radial basis function surrogate model and a local search procedure. The experimental results on seven benchmark functions of dimensions 30, 50, 100, and 200 show that the proposed LSADE algorithm is competitive compared with the state-of-the-art algorithms under a limited computational budget, being especially effective for the very complicated benchmark functions in high dimensions.
Flamingo: a Visual Language Model for Few-Shot Learning
Authors: Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan
Date: 2022-04-29 00:00:00
Proceeding: DeepMind 2022 4
Tasks: Few-Shot Learning, Generative Visual Question Answering, Language Modeling, Language Modelling, Medical Visual Question Answering, Meme Classification, Multiple-choice, Question Answering, Temporal/Casual QA, Video Question Answering, Video Understanding, Visual Question Answering, Visual Question Answering (VQA), Zero-Shot Cross-Modal Retrieval, Zero-Shot Learning, Zero-Shot Video Question Answer

Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data.
Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies
Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz
Date: 2022-04-29 00:00:00

We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed in polynomial time. In the dialect DL-LiteF, in contrast, frontiers may be infinite. We identify a natural syntactic restriction that enables the same positive results as for DL-LiteH. We use out results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact learning with only membership queries.
Tractable Uncertainty for Structure Learning
Authors: Benjie Wang, Matthew Wicker, Marta Kwiatkowska
Date: 2022-04-29 00:00:00

Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the representation of our posterior belief. In contrast to sample-based posterior approximations, our representation can capture a much richer space of DAGs, while also being able to tractably reason about the uncertainty through a range of useful inference queries. We empirically show how probabilistic circuits can be used as an augmented representation for structure learning methods, leading to improvement in both the quality of inferred structures and posterior uncertainty. Experimental results on conditional query answering further demonstrate the practical utility of the representational capacity of TRUST.
Training Language Models with Language Feedback
Authors: Jérémy Scheurer, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, Ethan Perez
Date: 2022-04-29 00:00:00
Tasks: Language Modeling, Language Modelling

Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human evaluation: comparisons between pairs of model-generated task outputs. Comparison feedback conveys limited information about human preferences per human evaluation. Here, we propose to learn from natural language feedback, which conveys more information per human evaluation. We learn from language feedback on model outputs using a three-step learning algorithm. First, we condition the language model on the initial output and feedback to generate many refinements. Second, we choose the refinement with the highest similarity to the feedback. Third, we finetune a language model to maximize the likelihood of the chosen refinement given the input. In synthetic experiments, we first evaluate whether language models accurately incorporate feedback to produce refinements, finding that only large language models (175B parameters) do so. Using only 100 samples of human-written feedback, our learning algorithm finetunes a GPT-3 model to roughly human-level summarization ability.
SATfeatPy - A Python-based Feature Extraction System for Satisfiability
Authors: Benjamin Provan-Bessell, Marco Dalla, Andrea Visentin, Barry O'Sullivan
Date: 2022-04-29 00:00:00

Feature extraction is a fundamental task in the application of machine learning methods to SAT solving. It is used in algorithm selection and configuration for solver portfolios and satisfiability classification. Many approaches have been proposed to extract meaningful attributes from CNF instances. Most of them lack a working/updated implementation, and the limited descriptions lack clarity affecting the reproducibility. Furthermore, the literature misses a comparison among the features. This paper introduces SATfeatPy, a library that offers feature extraction techniques for SAT problems in the CNF form. This package offers the implementation of all the structural and statistical features from there major papers in the field. The library is provided in an up-to-date, easy-to-use Python package alongside a detailed feature description. We show the high accuracy of SAT/UNSAT and problem category classification, using five sets of features generated using our library from a dataset of 3000 SAT and UNSAT instances, over ten different classes of problems. Finally, we compare the usefulness of the features and importance for predicting a SAT instance's original structure in an ablation study.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Authors: Yuting Gao, Jinfeng Liu, Zihan Xu, Jun Zhang, Ke Li, Rongrong Ji, Chunhua Shen
Date: 2022-04-29 00:00:00
Tasks: Image Classification, Language Modeling, Language Modelling, Object Detection, zero-shot-classification, Zero-Shot Image Classification, Zero-Shot Learning

Large-scale vision-language pre-training has achieved promising results on downstream tasks. Existing methods highly rely on the assumption that the image-text pairs crawled from the Internet are in perfect one-to-one correspondence. However, in real scenarios, this assumption can be difficult to hold: the text description, obtained by crawling the affiliated metadata of the image, often suffers from the semantic mismatch and the mutual compatibility. To address these issues, we introduce PyramidCLIP, which constructs an input pyramid with different semantic levels for each modality, and aligns visual elements and linguistic elements in the form of hierarchy via peer-level semantics alignment and cross-level relation alignment. Furthermore, we soften the loss of negative samples (unpaired samples) so as to weaken the strict constraint during the pre-training stage, thus mitigating the risk of forcing the model to distinguish compatible negative pairs. Experiments on five downstream tasks demonstrate the effectiveness of the proposed PyramidCLIP. In particular, with the same amount of 15 million pre-training image-text pairs, PyramidCLIP exceeds CLIP on ImageNet zero-shot classification top-1 accuracy by 10.6%/13.2%/10.0% with ResNet50/ViT-B32/ViT-B16 based image encoder respectively. When scaling to larger datasets, PyramidCLIP achieves the state-of-the-art results on several downstream tasks. In particular, the results of PyramidCLIP-ResNet50 trained on 143M image-text pairs surpass that of CLIP using 400M data on ImageNet zero-shot classification task, significantly improving the data efficiency of CLIP.