pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian outcome-guided multi-view mixture models with applications in molecular precision medicine
Authors: Paul D. W. Kirk, Filippo Pagani, Sylvia Richardson
Date: 2023-03-01 00:00:00
Tasks: Clustering

Clustering is commonly performed as an initial analysis step for uncovering structure in 'omics datasets, e.g. to discover molecular subtypes of disease. The high-throughput, high-dimensional nature of these datasets means that they provide information on a diverse array of different biomolecular processes and pathways. Different groups of variables (e.g. genes or proteins) will be implicated in different biomolecular processes, and hence undertaking analyses that are limited to identifying just a single clustering partition of the whole dataset is therefore liable to conflate the multiple clustering structures that may arise from these distinct processes. To address this, we propose a multi-view Bayesian mixture model that identifies groups of variables (``views"), each of which defines a distinct clustering structure. We consider applications in stratified medicine, for which our principal goal is to identify clusters of patients that define distinct, clinically actionable disease subtypes. We adopt the semi-supervised, outcome-guided mixture modelling approach of Bayesian profile regression that makes use of a response variable in order to guide inference toward the clusterings that are most relevant in a stratified medicine context. We present the model, together with illustrative simulation examples, and examples from pan-cancer proteomics. We demonstrate how the approach can be used to perform integrative clustering, and consider an example in which different 'omics datasets are integrated in the context of breast cancer subtyping.
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems
Authors: Adrien Corenflos, Simo Särkkä
Date: 2023-03-01 00:00:00
Tasks: State Space Models

Sampling from the full posterior distribution of high-dimensional non-linear, non-Gaussian latent dynamical models presents significant computational challenges. While Particle Gibbs (also known as conditional sequential Monte Carlo) is considered the gold standard for this task, it quickly degrades in performance as the latent space dimensionality increases. Conversely, globally Gaussian-approximated methods like extended Kalman filtering, though more robust, are seldom used for posterior sampling due to their inherent bias. We introduce novel auxiliary sampling approaches that address these limitations. By incorporating artificial observations of the system as auxiliary variables in our MCMC kernels, we develop both efficient exact Kalman-based samplers and enhanced Particle Gibbs algorithms that maintain performance in high-dimensional latent spaces. Some of our methods support parallelisation along the time dimension, achieving logarithmic scaling when implemented on GPUs. Empirical evaluations demonstrate superior statistical and computational performance compared to existing approaches for high-dimensional latent dynamical systems.
Authorship Conflicts in Academia: an International Cross-Discipline Survey
Authors: Elizaveta Savchenko, Ariel Rosenfeld
Date: 2023-03-01 00:00:00
Tasks: Survey

Collaboration among scholars has emerged as a significant characteristic of contemporary science. As a result, the number of authors listed in publications continues to rise steadily. Unfortunately, determining the authors to be included in the byline and their respective order entails multiple difficulties which often lead to conflicts. Despite the large volume of literature about conflicts in academia, it remains unclear how exactly it is distributed over the main socio-demographic properties, as well as the different types of interactions academics experience. To address this gap, we conducted an international and cross-disciplinary survey answered by 752 academics from 41 fields of research and 93 countries that statistically well-represent the overall academic workforce. Our findings are concerning and suggest that authorship credit conflicts arise very early in one's academic career, even at the level of Master and Ph.D., and become increasingly common over time.
Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System
Authors: Shanshan Lyu, Qiwei Chen, Tao Zhuang, Junfeng Ge
Date: 2023-03-01 00:00:00
Tasks: Recommendation Systems, Selection bias

Recommender system is an essential part of online services, especially for e-commerce platform. Conversion Rate (CVR) prediction in RS plays a significant role in optimizing Gross Merchandise Volume (GMV) goal of e-commerce. However, CVR suffers from well-known Sample Selection Bias (SSB) and Data Sparsity (DS) problems. Although existing methods ESMM and ESM2 train with all impression samples over the entire space by modeling user behavior paths, SSB and DS problems still exist. In real practice, the online inference space are samples from previous stage of RS process, rather than the impression space modeled by existing methods. Moreover, existing methods solve the DS problem mainly by building behavior paths of their own specific scene, ignoring the behaviors in various scenes of e-commerce platform. In this paper, we propose Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System, solving SSB and DS problems by reformulating GMV goal in a novel manner. Specifically, we rebuild the CVR on the entire data space with samples from previous stage of RS process, unifying training and online inference space. Moreover, we explicitly introduce purchase samples from other scenes of e-commerce platform in model learning process. Online A/B test and offline experiments show the superiority of our framework. Our framework has been deployed in rank stage of Taobao recommendation, providing recommendation service for hundreds of millions of consumers everyday.
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation
Authors: Xu Chen, Jingsen Zhang, Lei Wang, Quanyu Dai, Zhenhua Dong, Ruiming Tang, Rui Zhang, Li Chen, Ji-Rong Wen
Date: 2023-03-01 00:00:00
Tasks: Explainable Recommendation, Informativeness, Persuasiveness

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, informativeness and user satisfaction. Despite a lot of promising explainable recommender models have been proposed in the past few years, the evaluation strategies of these models suffer from several limitations. For example, the explanation ground truths are not labeled by real users, the explanations are mostly evaluated based on only one aspect and the evaluation strategies can be hard to unify. To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths. In specific, we firstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 users with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where ten well-known explainable recommender models are implemented in a unified framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to shed some new lights to the explainable recommendation field. Our dataset, library and the related documents have been released at https://reasoner2023.github.io/.
VinVL+L: Enriching Visual Representation with Location Context in VQA
Authors: Jiří Vyskočil, Lukáš Picek
Date: 2023-02-22 00:00:00
Proceeding: Computer Vision Winter Workshop 2023 2
Tasks: Question Answering, TAG, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we describe a novel method - VinVL+L - that enriches the visual representations (i.e. object tags and region features) of the State-of-the-Art Vision and Language (VL) method - VinVL - with Location information. To verify the importance of such metadata for VL models, we (i) trained a Swin-B model on the Places365 dataset and obtained additional sets of visual and tag features; both were made public to allow reproducibility and further experiments, (ii) did an architectural update to the existing VinVL method to include the new feature sets, and (iii) provide a qualitative and quantitative evaluation. By including just binary location metadata, the VinVL+L method provides incremental improvement to the State-of-the-Art VinVL in Visual Question Answering (VQA). The VinVL+L achieved an accuracy of 64.85% and increased the performance by +0.32% in terms of accuracy on the GQA dataset; the statistical significance of the new representations is verified via Approximate Randomization.
The code and newly generated sets of features are available at https://github.com/vyskocj/VinVL-L.
Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions
Authors: Ruibin Ma, Rui Wang, Stephen Pizer, Julian Rosenman, Sarah K. McGill, and Jan-Michael Frahm
Date: 2019-10-10 00:00:00
Proceeding: Medical Image Computing and Computer Assisted Intervention 2019 10
Tasks: 3D Reconstruction, Simultaneous Localization and Mapping

Colonoscopy is the most widely used medical technique to screen the human large intestine (colon) for cancer precursors. However, frequently parts of the surface are not visualized, and it is hard for the endoscopist to realize that from the video. Non-visualization derives from lack of orientations of the endoscope to the full circumference of parts of the colon, occlusion from colon structures, and intervening materials inside the colon. Our solution is real-time dense 3D reconstruction of colon chunks with display of the missing regions. We accomplish this by a novel deep-learning-driven dense SLAM (simultaneous localization and mapping) system that can produce a camera trajectory and a dense reconstructed surface for colon chunks (small lengths of colon). Traditional SLAM systems work poorly for the low-textured colonoscopy frames and are subject to severe scale/camera drift. In our method a recurrent neural network (RNN) is used to predict scale-consistent depth maps and camera poses of successive frames. These outputs are incorporated into a standard SLAM pipeline with local windowed optimization. The depth maps are ﬁnally fused into a global surface using the optimized camera poses. To the best of our knowledge, we are the ﬁrst to reconstruct dense colon surface from video in real time and to display missing surface.
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
Authors: Tongle Fan, Guanglei Wang, Yan Li, Hongrui Wang
Date: 2020-09-21 00:00:00
Proceeding: IEEE Access 2020 9
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation. Unlike the previous works which extract the context information of medical image via applying the multi-scale feature fusion, we propose a novel network named Multi-scale Attention Net (MA-Net) by introducing self-attention mechanism into our method to adaptively integrate local features with their global dependencies. The MA-Net can capture rich contextual dependencies based on the attention mechanism. We design two blocks: Position-wise Attention Block (PAB) and Multi-scale Fusion Attention Block (MFAB). The PAB is used to model the feature interdependencies in spatial dimensions, which capture the spatial dependencies between pixels in a global view. In addition, the MFAB is to capture the channel dependencies between any feature map by multi-scale semantic feature fusion. We evaluate our method on the dataset of MICCAI 2017 LiTS Challenge. The proposed method achieves better performance than other state-of-the-art methods. The Dice values of liver and tumors segmentation are 0.960 ± 0.03 and 0.749 ± 0.08 respectively.
A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data
Authors: DEVRİM AKGÜN, ABDULLAH TALHA KABAKUŞ, ZEHRA KARAPINAR ŞENTÜRK, ARAFAT ŞENTÜRK, ENVER KÜÇÜKKÜLAHLI
Date: 2021-12-01 00:00:00
Proceeding: journal 2021 12
Tasks: COVID-19 Diagnosis, Transfer Learning

The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study. The proposed models employed various pretrained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer learning technique, provided the best accuracy, 86.42%, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.
Quantifying dimensional change in stochastic portfolio theory
Authors: Erhan Bayraktar, Donghan Kim, Abhishek Tilva
Date: 2023-03-01 00:00:00

In this paper, we develop the theory of functional generation of portfolios in an equity market with changing dimension. By introducing dimensional jumps in the market, as well as jumps in stock capitalization between the dimensional jumps, we construct different types of self-financing stock portfolios (additive, multiplicative, and rank-based) in a very general setting. Our study explains how a dimensional change caused by a listing or delisting event of a stock, and unexpected shocks in the market, affect portfolio return. We also provide empirical analyses of some classical portfolios, quantifying the impact of dimensional change in portfolio performance relative to the market.
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation
Authors: Zicheng Wang, Zhen Zhao, Xiaoxia Xing, Dong Xu, Xiangyu Kong, Luping Zhou
Date: 2023-03-02 00:00:00
Proceeding: CVPR 2023 1
Tasks: Semantic Segmentation, Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC.
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
Authors: Xiaoguang Chang, Teng Wang, Shaowei Cai, Changyin Sun
Date: 2023-03-02 00:00:00
Tasks: Graph Generation, Object, Representation Learning, Scene Graph Generation, Word Embeddings

Scene graph generation (SGG) is a sophisticated task that suffers from both complex visual features and dataset long-tail problem. Recently, various unbiased strategies have been proposed by designing novel loss functions and data balancing strategies. Unfortunately, these unbiased methods fail to emphasize language priors in feature refinement perspective. Inspired by the fact that predicates are highly correlated with semantics hidden in subject-object pair and global context, we propose LANDMARK (LANguage-guiDed representationenhanceMent frAmewoRK) that learns predicate-relevant representations from language-vision interactive patterns, global language context and pair-predicate correlation. Specifically, we first project object labels to three distinctive semantic embeddings for different representation learning. Then, Language Attention Module (LAM) and Experience Estimation Module (EEM) process subject-object word embeddings to attention vector and predicate distribution, respectively. Language Context Module (LCM) encodes global context from each word embed-ding, which avoids isolated learning from local information. Finally, modules outputs are used to update visual representations and SGG model's prediction. All language representations are purely generated from object categories so that no extra knowledge is needed. This framework is model-agnostic and consistently improves performance on existing SGG models. Besides, representation-level unbiased strategies endow LANDMARK the advantage of compatibility with other methods. Code is available at https://github.com/rafa-cxg/PySGG-cxg.
Computational Language Acquisition with Theory of Mind
Authors: Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig
Date: 2023-03-02 00:00:00
Tasks: Language Acquisition

Unlike current state-of-the-art language models, young children actively acquire language through interactions with their surrounding environment and caretakers. One mechanism that has been argued to be critical to language learning is the ability to infer the mental states of other agents in social environments, coined Theory of Mind (ToM) by Premack & Woodruff (1978). Drawing inspiration from the modern operationalized versions of ToM implemented in Rabinowitz et al. (2018) and Zhu et al. (2021), we build language-learning agents equipped with ToM, and measure its effects on the learning process. We model ToM by giving the speaker agent an internal listener model that is trained alongside the speaker and used to rerank potential utterances. We experiment with varying task difficulty, hypothesizing that models will acquire more complex language to adapt to stronger environmental pressures. We find that training speakers with a highly weighted ToM listener component leads to performance gains in our image referential game setting. We also find some evidence that increasing task difficulty in the training process results in more fluent and precise utterances in evaluation. This suggests the potential utility of further incorporating ToM, as well as other insights from child language acquisition, into computational models of language acquisition.
Dropout Reduces Underfitting
Authors: Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell
Date: 2023-03-02 00:00:00

Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout can also mitigate underfitting when used at the start of training. During the early phase, we find dropout reduces the directional variance of gradients across mini-batches and helps align the mini-batch gradients with the entire dataset's gradient. This helps counteract the stochasticity of SGD and limit the influence of individual batches on model training. Our findings lead us to a solution for improving performance in underfitting models - early dropout: dropout is applied only during the initial phases of training, and turned off afterwards. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. Additionally, we explore a symmetric technique for regularizing overfitting models - late dropout, where dropout is not used in the early iterations and is only activated later in training. Experiments on ImageNet and various vision tasks demonstrate that our methods consistently improve generalization accuracy. Our results encourage more research on understanding regularization in deep learning and our methods can be useful tools for future neural network training, especially in the era of large data. Code is available at https://github.com/facebookresearch/dropout.
Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations
Authors: Siddhant Haldar, Jyothish Pari, Anant Rai, Lerrel Pinto
Date: 2023-03-02 00:00:00
Tasks: Imitation Learning

While imitation learning provides us with an efficient toolkit to train robots, learning skills that are robust to environment variations remains a significant challenge. Current approaches address this challenge by relying either on large amounts of demonstrations that span environment variations or on handcrafted reward functions that require state estimates. Both directions are not scalable to fast imitation. In this work, we present Fast Imitation of Skills from Humans (FISH), a new imitation learning approach that can learn robust visual skills with less than a minute of human demonstrations. Given a weak base-policy trained by offline imitation of demonstrations, FISH computes rewards that correspond to the "match" between the robot's behavior and the demonstrations. These rewards are then used to adaptively update a residual policy that adds on to the base-policy. Across all tasks, FISH requires at most twenty minutes of interactive learning to imitate demonstrations on object configurations that were not seen in the demonstrations. Importantly, FISH is constructed to be versatile, which allows it to be used across robot morphologies (e.g. xArm, Allegro, Stretch) and camera configurations (e.g. third-person, eye-in-hand). Our experimental evaluations on 9 different tasks show that FISH achieves an average success rate of 93%, which is around 3.8x higher than prior state-of-the-art methods.