pwc-archive/papers-with-abstracts huggingface dataset:
Deep embedded clustering algorithm for clustering PACS repositories
Authors: Teo Manojlović, Matija Milanič, Ivan Štajduhar
Date: 2022-06-24 00:00:00
Tasks: Clustering, Image Clustering, TAG

Creating large datasets of medical radiology images from several sources can be challenging because of the differences in the acquisition and storage standards. One possible way of controlling and/or assessing the image selection process is through medical image clustering. This, however, requires an efficient method for learning latent image representations. In this paper, we tackle the problem of fully-unsupervised clustering of medical images using pixel data only. We test the performance of several contemporary approaches, built on top of a convolutional autoencoder (CAE) - convolutional deep embedded clustering (CDEC) and convolutional improved deep embedded clustering (CIDEC) - and three approaches based on preset feature extraction - histogram of oriented gradients (HOG), local binary pattern (LBP) and principal component analysis (PCA). CDEC and CIDEC are end-to-end clustering solutions, involving simultaneous learning of latent representations and clustering assignments, whereas the remaining approaches rely on k-means clustering from fixed embeddings. We train the models on 30,000 images, and test them using a separate test set consisting of 8,000 images. We sampled the data from the PACS repository archive of the Clinical Hospital Centre Rijeka. For evaluation, we use silhouette score, homogeneity score and normalised mutual information (NMI) on two target parameters, closely associated with commonly occurring DICOM tags - Modality and anatomical region (adjusted BodyPartExamined tag). CIDEC attains an NMI score of 0.473 with respect to anatomical region, and CDEC attains an NMI score of 0.645 with respect to the tag Modality - both outperforming other commonly used feature descriptors.
Independent evaluation of state-of-the-art deep networks for mammography
Authors: Osvaldo Matias Velarde, Lucas Parrra
Date: 2022-06-22 00:00:00

Deep neural models have shown remarkable performance in image recognition tasks, whenever large datasets of labeled images are available. The largest datasets in radiology are available for screening mammography. Recent reports, including in high impact journals, document performance of deep models at or above that of trained radiologists. What is not yet known is whether performance of these trained models is robust and replicates across datasets. Here we evaluate performance of five published state-of-the-art models on four publicly available mammography datasets. The limited size of public datasets precludes retraining the model and so we are limited to evaluate those models that have been made available with pre-trained parameters. Where test data was available, we replicated published results. However, the trained models performed poorly on out-of-sample data, except when based on all four standard views of a mammographic exam. We conclude that future progress will depend on a concerted effort to make more diverse and larger mammography datasets publicly available. Meanwhile, results that are not accompanied by a release of trained models for independent validation should be judged cautiously.
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Authors: Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan, Qiang Yang
Date: 2022-06-21 00:00:00
Tasks: Face Recognition, Federated Learning, Transfer Learning

Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology. However, recent regulatory restrictions on data privacy preclude uploading sensitive local data to data centers and utilizing them in a centralized approach. Directly applying federated learning algorithms in this scenario could hardly meet the industrial requirements of both efficiency and accuracy. Therefore, we propose an efficient industrial federated learning framework for AIoT in terms of a face recognition application. Specifically, we propose to utilize the concept of transfer learning to speed up federated training on devices and further present a novel design of a private projector that helps protect shared gradients without incurring additional memory consumption or computational cost. Empirical studies on a private Asian face dataset show that our approach can achieve high recognition accuracy in only 20 communication rounds, demonstrating its effectiveness in prediction and its efficiency in training.
Generative Modelling With Inverse Heat Dissipation
Authors: Severi Rissanen, Markus Heinonen, Arno Solin
Date: 2022-06-21 00:00:00
Tasks: Disentanglement, Image Generation, Inductive Bias

While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them.
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
Authors: Brandon Trabucco, Gunnar Sigurdsson, Robinson Piramuthu, Gaurav S. Sukhatme, Ruslan Salakhutdinov
Date: 2022-06-21 00:00:00
Tasks: Semantic Segmentation

Physically rearranging objects is an important capability for embodied agents. Visual room rearrangement evaluates an agent's ability to rearrange objects in a room to a desired goal based solely on visual input. We propose a simple yet effective method for this problem: (1) search for and map which objects need to be rearranged, and (2) rearrange each object until the task is complete. Our approach consists of an off-the-shelf semantic segmentation model, voxel-based semantic map, and semantic search policy to efficiently find objects that need to be rearranged. On the AI2-THOR Rearrangement Challenge, our method improves on current state-of-the-art end-to-end reinforcement learning-based methods that learn visual rearrangement policies from 0.53% correct rearrangement to 16.56%, using only 2.7% as many samples from the environment.
Open Set Classification of Untranscribed Handwritten Documents
Authors: José Ramón Prieto, Juan José Flores, Enrique Vidal, Alejandro H. Toselli, David Garrido, Carlos Alonso
Date: 2022-06-20 00:00:00
Tasks: Classification, open-set classification, TAG

Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The amounts are so large that it is generally unfeasible for archivists to adequately tag most of the documents with the required metadata so as to low proper organization of the archives and effective exploration by scholars and the general public. The class or ``typology'' of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The approach considered is based on ``probabilistic indexing'', a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex notarial manuscripts from the Spanish Archivo Host\'orico Provincial de C\'adiz, with promising results.
Gait Cycle Reconstruction and Human Identification from Occluded Sequences
Authors: Abhishek Paul, Manav Mukesh Jain, Jinesh Jain, Pratik Chattopadhyay
Date: 2022-06-20 00:00:00
Tasks: Gait Recognition, Occlusion Handling, Person Identification

Gait-based person identification from videos captured at surveillance sites using Computer Vision-based techniques is quite challenging since these walking sequences are usually corrupted with occlusion, and a complete cycle of gait is not always available. In this work, we propose an effective neural network-based model to reconstruct the occluded frames in an input sequence before carrying out gait recognition. Specifically, we employ LSTM networks to predict an embedding for each occluded frame both from the forward and the backward directions, and next fuse the predictions from the two LSTMs by employing a network of residual blocks and convolutional layers. While the LSTMs are trained to minimize the mean-squared loss, the fusion network is trained to optimize the pixel-wise cross-entropy loss between the ground-truth and the reconstructed samples. Evaluation of our approach has been done using synthetically occluded sequences generated from the OU-ISIR LP and CASIA-B data and real-occluded sequences present in the TUM-IITKGP data. The effectiveness of the proposed reconstruction model has been verified through the Dice score and gait-based recognition accuracy using some popular gait recognition methods. Comparative study with existing occlusion handling methods in gait recognition highlights the superiority of our proposed occlusion reconstruction approach over the others.
Supply-Side Equilibria in Recommender Systems
Authors: Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt
Date: 2022-06-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Recommendation Systems

Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also producer incentives. Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content. In this work, we investigate the resulting supply-side equilibria in personalized content recommender systems. We model users and content as $D$-dimensional vectors, the recommendation algorithm as showing each user the content with highest dot product, and producers as maximizing the number of users who are recommended their content minus the cost of production. Two key features of our model are that the producer decision space is multi-dimensional and the user base is heterogeneous, which contrasts with classical low-dimensional models. Multi-dimensionality and heterogeneity create the potential for specialization, where different producers create different types of content at equilibrium. Using a duality argument, we derive necessary and sufficient conditions for whether specialization occurs: these conditions depend on the extent to which users are heterogeneous and to which producers can perform well on all dimensions at once without incurring a high cost. Then, we characterize the distribution of content at equilibrium in concrete settings with two populations of users. Lastly, we show that specialization can enable producers to achieve positive profit at equilibrium, which means that specialization can reduce the competitiveness of the marketplace. At a conceptual level, our analysis of supply-side competition takes a step towards elucidating how personalized recommendations shape the marketplace of digital goods, and towards understanding what new phenomena arise in multi-dimensional competitive settings.
Positive-definite parametrization of mixed quantum states with deep neural networks
Authors: Filippo Vicentini, Riccardo Rossi, Giuseppe Carleo
Date: 2022-06-27 00:00:00

We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with polynomial resources. We then show how to embed an autoregressive structure in the GHDO to allow direct sampling of the probability distribution. These properties are especially important when representing and variationally optimizing the mixed quantum state of a system interacting with an environment. Finally, we benchmark this architecture by simulating the steady state of the dissipative transverse-field Ising model. Estimating local observables and the R\'enyi entropy, we show significant improvements over previous state-of-the-art variational approaches.
Understanding Benign Overfitting in Gradient-Based Meta Learning
Authors: Lisha Chen, Songtao Lu, Tianyi Chen
Date: 2022-06-27 00:00:00
Tasks: Few-Shot Learning, Learning Theory, Meta-Learning

Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conventional statistical learning theory suggests that overparameterized models tend to overfit, empirical evidence reveals that overparameterized meta learning methods still work well -- a phenomenon often called "benign overfitting." To understand this phenomenon, we focus on the meta learning settings with a challenging bilevel structure that we term the gradient-based meta learning, and analyze its generalization performance under an overparameterized meta linear regression model. While our analysis uses the relatively tractable linear models, our theory contributes to understanding the delicate interplay among data heterogeneity, model adaptation and benign overfitting in gradient-based meta learning tasks. We corroborate our theoretical claims through numerical simulations.
Thermodynamics-inspired Explanations of Artificial Intelligence
Authors: Shams Mehdi, Pratyush Tiwary
Date: 2022-06-27 00:00:00
Tasks: feature selection, image-classification, Image Classification, text-classification, Text Classification

In recent years, predictive machine learning methods have gained prominence in various scientific domains. However, due to their black-box nature, it is essential to establish trust in these models before accepting them as accurate. One promising strategy for assigning trust involves employing explanation techniques that elucidate the rationale behind a black-box model's predictions in a manner that humans can understand. However, assessing the degree of human interpretability of the rationale generated by such methods is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for assessing the degree of human interpretability associated with any linear model. Using this concept and drawing inspiration from classical thermodynamics, we present Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms (TERP), a method for generating accurate, and human-interpretable explanations for black-box predictions in a model-agnostic manner. To demonstrate the wide-ranging applicability of TERP, we successfully employ it to explain various black-box model architectures, including deep learning Autoencoders, Recurrent Neural Networks, and Convolutional Neural Networks, across diverse domains such as molecular simulations, text, and image classification.
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
Authors: Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
Date: 2022-06-27 00:00:00

To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.
Causal Dynamics Learning for Task-Independent State Abstraction
Authors: Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, Peter Stone
Date: 2022-06-27 00:00:00
Tasks: Model-based Reinforcement Learning

Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states. In this paper, we introduce Causal Dynamics Learning for Task-Independent State Abstraction (CDL), which first learns a theoretically proved causal dynamics model that removes unnecessary dependencies between state variables and the action, thus generalizing well to unseen states. A state abstraction can then be derived from the learned dynamics, which not only improves sample efficiency but also applies to a wider range of tasks than existing state abstraction methods. Evaluated on two simulated environments and downstream tasks, both the dynamics model and policies learned by the proposed method generalize well to unseen states and the derived state abstraction improves sample efficiency compared to learning without it.
Pen and Paper Exercises in Machine Learning
Authors: Michael U. Gutmann
Date: 2022-06-27 00:00:00
Tasks: BIG-bench Machine Learning

This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Authors: Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré La Tour, Ghislain Durif, Cassio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoit Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter
Date: 2022-06-27 00:00:00
Tasks: Benchmarking, image-classification, Image Classification, Stochastic Optimization

Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Authors: Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison
Date: 2022-06-27 00:00:00
Tasks: Imitation Learning

Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
Authors: Georg Siedel, Silvia Vock, Andrey Morozov, Stefan Voß
Date: 2022-06-27 00:00:00
Tasks: Data Augmentation

Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating corruption robustness in a way that allows comparability and interpretability on a given dataset. We propose a test data augmentation method that uses a robustness distance $\epsilon$ derived from the datasets minimal class separation distance. The resulting MSCR (mean statistical corruption robustness) metric allows a dataset-specific comparison of different classifiers with respect to their corruption robustness. The MSCR value is interpretable, as it represents the classifiers avoidable loss of accuracy due to statistical corruptions. On 2D and image data, we show that the metric reflects different levels of classifier robustness. Furthermore, we observe unexpected optima in classifiers robust accuracy through training and testing classifiers with different levels of noise. While researchers have frequently reported on a significant tradeoff on accuracy when training robust models, we strengthen the view that a tradeoff between accuracy and corruption robustness is not inherent. Our results indicate that robustness training through simple data augmentation can already slightly improve accuracy.