pwc-archive/papers-with-abstracts huggingface dataset:
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Authors: Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens
Date: 2023-08-30 00:00:00

Multitask learning (MTL) leverages task-relatedness to enhance performance. With the emergence of multimodal data, tasks can now be referenced by multiple indices. In this paper, we employ high-order tensors, with each mode corresponding to a task index, to naturally represent tasks referenced by multiple indices and preserve their structural relations. Based on this representation, we propose a general framework of low-rank MTL methods with tensorized support vector machines (SVMs) and least square support vector machines (LSSVMs), where the CP factorization is deployed over the coefficient tensor. Our approach allows to model the task relation through a linear combination of shared factors weighted by task-specific factors and is generalized to both classification and regression problems. Through the alternating optimization scheme and the Lagrangian function, each subproblem is transformed into a convex problem, formulated as a quadratic programming or linear system in the dual form. In contrast to previous MTL frameworks, our decision function in the dual induces a weighted kernel function with a task-coupling term characterized by the similarities of the task-specific factors, better revealing the explicit relations across tasks in MTL. Experimental results validate the effectiveness and superiority of our proposed methods compared to existing state-of-the-art approaches in MTL. The code of implementation will be available at https://github.com/liujiani0216/TSVM-MTL.
PAVI: Plate-Amortized Variational Inference
Authors: Louis Rouillard, Alexandre Le Bris, Thomas Moreau, Demian Wassermann
Date: 2023-08-30 00:00:00
Tasks: Bayesian Inference, Variational Inference

Given observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is challenging for large population studies where millions of measurements are performed over a cohort of hundreds of subjects, resulting in a massive parameter space. This large cardinality renders off-the-shelf Variational Inference (VI) computationally impractical. In this work, we design structured VI families that efficiently tackle large population studies. Our main idea is to share the parameterization and learning across the different i.i.d. variables in a generative model, symbolized by the model's \textit{plates}. We name this concept \textit{plate amortization}. Contrary to off-the-shelf stochastic VI, which slows down inference, plate amortization results in orders of magnitude faster to train variational distributions. Applied to large-scale hierarchical problems, PAVI yields expressive, parsimoniously parameterized VI with an affordable training time. This faster convergence effectively unlocks inference in those large regimes. We illustrate the practical utility of PAVI through a challenging Neuroimaging example featuring 400 million latent parameters, demonstrating a significant step towards scalable and expressive Variational Inference.
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality
Authors: Peizheng Li, Adnan Aijaz, Tim Farnham, Sajida Gufran, Sita Chintalapati
Date: 2023-08-30 00:00:00
Tasks: Anomaly Detection

Recently, the concept of digital twins (DTs) has received significant attention within the realm of 5G/6G. This demonstration shows an innovative DT design and implementation framework tailored toward integration within the 5G infrastructure. The proposed DT enables near real-time anomaly detection capability pertaining to user connectivity. It empowers the 5G system to proactively execute decisions for resource control and connection restoration.
Jaccard-constrained dense subgraph discovery
Authors: Chamalee Wickrama Arachchi, Nikolaj Tatti
Date: 2023-08-30 00:00:00
Tasks: 2k, Graph Mining

Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset can be represented as a sequence of graph snapshots. Hence, it is natural to consider the question of finding dense subgraphs in a temporal network that are allowed to vary over time to a certain degree. In this paper, we search for dense subgraphs that have large pairwise Jaccard similarity coefficients. More formally, given a set of graph snapshots and a weight $\lambda$, we find a collection of dense subgraphs such that the sum of densities of the induced subgraphs plus the sum of Jaccard indices, weighted by $\lambda$, is maximized. We prove that this problem is NP-hard. To discover dense subgraphs with good objective value, we present an iterative algorithm which runs in $\mathcal{O}(n^2k^2 + m \log n + k^3 n)$ time per single iteration, and a greedy algorithm which runs in $\mathcal{O}(n^2k^2 + m \log n + k^3 n)$ time, where $k$ is the length of the graph sequence and $n$ and $m$ denote number of nodes and total number of edges respectively. We show experimentally that our algorithms are efficient, they can find ground truth in synthetic datasets and provide interpretable results from real-world datasets. Finally, we present a case study that shows the usefulness of our problem.
LLaSM: Large Language and Speech Model
Authors: Yu Shu, Siwei Dong, Guangyao Chen, Wenhao Huang, Ruihua Zhang, Daochen Shi, Qiqi Xiang, Yemin Shi
Date: 2023-08-30 00:00:00
Tasks: Instruction Following, Language Modeling, Language Modelling, model

Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions.
Towards One-Shot Learning for Text Classification using Inductive Logic Programming
Authors: Ghazal Afroozi Milani, Daniel Cyrus, Alireza Tamaddoni-Nezhad
Date: 2023-08-30 00:00:00
Tasks: Classification, Common Sense Reasoning, Inductive logic programming, One-Shot Learning, text-classification, Text Classification

With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data. In this paper, we explore an Inductive Logic Programming approach for one-shot text classification. In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet. Results indicate that MIL can learn text classification rules from a small number of training examples. Moreover, the higher complexity of chosen examples, the higher accuracy of the outcome.
Minimum Width for Deep, Narrow MLP: A Diffeomorphism Approach
Authors: Geonho Hwang
Date: 2023-08-30 00:00:00

Recently, there has been a growing focus on determining the minimum width requirements for achieving the universal approximation property in deep, narrow Multi-Layer Perceptrons (MLPs). Among these challenges, one particularly challenging task is approximating a continuous function under the uniform norm, as indicated by the significant disparity between its lower and upper bounds. To address this problem, we propose a framework that simplifies finding the minimum width for deep, narrow MLPs into determining a purely geometrical function denoted as $w(d_x, d_y)$. This function relies solely on the input and output dimensions, represented as $d_x$ and $d_y$, respectively. Two key steps support this framework. First, we demonstrate that deep, narrow MLPs, when provided with a small additional width, can approximate a $C^2$-diffeomorphism. Subsequently, using this result, we prove that $w(d_x, d_y)$ equates to the optimal minimum width required for deep, narrow MLPs to achieve universality. By employing the aforementioned framework and the Whitney embedding theorem, we provide an upper bound for the minimum width, given by $\operatorname{max}(2d_x+1, d_y) + \alpha(\sigma)$, where $0 \leq \alpha(\sigma) \leq 2$ represents a constant depending on the activation function. Furthermore, we provide a lower bound of $4$ for the minimum width in cases where the input and output dimensions are both equal to two.
Domain Generalization without Excess Empirical Risk
Authors: Ozan Sener, Vladlen Koltun
Date: 2023-08-30 00:00:00
Tasks: Domain Generalization

Given data from diverse sets of distinct distributions, domain generalization aims to learn models that generalize to unseen distributions. A common approach is designing a data-driven surrogate penalty to capture generalization and minimize the empirical risk jointly with the penalty. We argue that a significant failure mode of this recipe is an excess risk due to an erroneous penalty or hardness in joint optimization. We present an approach that eliminates this problem. Instead of jointly minimizing empirical risk with the penalty, we minimize the penalty under the constraint of optimality of the empirical risk. This change guarantees that the domain generalization penalty cannot impair optimization of the empirical risk, i.e., in-distribution performance. To solve the proposed optimization problem, we demonstrate an exciting connection to rate-distortion theory and utilize its tools to design an efficient method. Our approach can be applied to any penalty-based domain generalization method, and we demonstrate its effectiveness by applying it to three examplar methods from the literature, showing significant improvements.
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
Authors: Masaaki Takada, Hironori Fujisawa
Date: 2023-08-30 00:00:00
Tasks: Variable Selection

This paper presents a comprehensive exploration of the theoretical properties inherent in the Adaptive Lasso and the Transfer Lasso. The Adaptive Lasso, a well-established method, employs regularization divided by initial estimators and is characterized by asymptotic normality and variable selection consistency. In contrast, the recently proposed Transfer Lasso employs regularization subtracted by initial estimators with the demonstrated capacity to curtail non-asymptotic estimation errors. A pivotal question thus emerges: Given the distinct ways the Adaptive Lasso and the Transfer Lasso employ initial estimators, what benefits or drawbacks does this disparity confer upon each method? This paper conducts a theoretical examination of the asymptotic properties of the Transfer Lasso, thereby elucidating its differentiation from the Adaptive Lasso. Informed by the findings of this analysis, we introduce a novel method, one that amalgamates the strengths and compensates for the weaknesses of both methods. The paper concludes with validations of our theory and comparisons of the methods via simulation experiments.
HAlf-MAsked Model for Named Entity Sentiment analysis
Authors: Anton Kabaev, Pavel Podberezko, Andrey Kaznacheev, Sabina Abdullayeva
Date: 2023-08-30 00:00:00
Tasks: model, Sentiment Analysis

Named Entity Sentiment analysis (NESA) is one of the most actively developing application domains in Natural Language Processing (NLP). Social media NESA is a significant field of opinion analysis since detecting and tracking sentiment trends in the news flow is crucial for building various analytical systems and monitoring the media image of specific people or companies. In this paper, we study different transformers-based solutions NESA in RuSentNE-23 evaluation. Despite the effectiveness of the BERT-like models, they can still struggle with certain challenges, such as overfitting, which appeared to be the main obstacle in achieving high accuracy on the RuSentNE-23 data. We present several approaches to overcome this problem, among which there is a novel technique of additional pass over given data with masked entity before making the final prediction so that we can combine logits from the model when it knows the exact entity it predicts sentiment for and when it does not. Utilizing this technique, we ensemble multiple BERT- like models trained on different subsets of data to improve overall performance. Our proposed model achieves the best result on RuSentNE-23 evaluation data and demonstrates improved consistency in entity-level sentiment analysis.
Split Without a Leak: Reducing Privacy Leakage in Split Learning
Authors: Khoa Nguyen, Tanveer Khan, Antonis Michalas
Date: 2023-08-30 00:00:00
Tasks: Privacy Preserving, Privacy Preserving Deep Learning

The popularity of Deep Learning (DL) makes the privacy of sensitive data more imperative than ever. As a result, various privacy-preserving techniques have been implemented to preserve user data privacy in DL. Among various privacy-preserving techniques, collaborative learning techniques, such as Split Learning (SL) have been utilized to accelerate the learning and prediction process. Initially, SL was considered a promising approach to data privacy. However, subsequent research has demonstrated that SL is susceptible to many types of attacks and, therefore, it cannot serve as a privacy-preserving technique. Meanwhile, countermeasures using a combination of SL and encryption have also been introduced to achieve privacy-preserving deep learning. In this work, we propose a hybrid approach using SL and Homomorphic Encryption (HE). The idea behind it is that the client encrypts the activation map (the output of the split layer between the client and the server) before sending it to the server. Hence, during both forward and backward propagation, the server cannot reconstruct the client's input data from the intermediate activation map. This improvement is important as it reduces privacy leakage compared to other SL-based works, where the server can gain valuable information about the client's input. In addition, on the MIT-BIH dataset, our proposed hybrid approach using SL and HE yields faster training time (about 6 times) and significantly reduced communication overhead (almost 160 times) compared to other HE-based approaches, thereby offering improved privacy protection for sensitive data in DL.
Fully Embedded Time-Series Generative Adversarial Networks
Authors: Joe Beck, Subhadeep Chakraborty
Date: 2023-08-30 00:00:00
Tasks: Time Series

Generative Adversarial Networks (GANs) should produce synthetic data that fits the underlying distribution of the data being modeled. For real valued time-series data, this implies the need to simultaneously capture the static distribution of the data, but also the full temporal distribution of the data for any potential time horizon. This temporal element produces a more complex problem that can potentially leave current solutions under-constrained, unstable during training, or prone to varying degrees of mode collapse. In FETSGAN, entire sequences are translated directly to the generator's sampling space using a seq2seq style adversarial auto encoder (AAE), where adversarial training is used to match the training distribution in both the feature space and the lower dimensional sampling space. This additional constraint provides a loose assurance that the temporal distribution of the synthetic samples will not collapse. In addition, the First Above Threshold (FAT) operator is introduced to supplement the reconstruction of encoded sequences, which improves training stability and the overall quality of the synthetic data being generated. These novel contributions demonstrate a significant improvement to the current state of the art for adversarial learners in qualitative measures of temporal similarity and quantitative predictive ability of data generated through FETSGAN.
Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations
Authors: Chetraj Pandey, Anli Ji, Trisha Nandakumar, Rafal A. Angryk, Berkay Aydin
Date: 2023-08-30 00:00:00
Tasks: Solar Flare Prediction

This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond $\pm$70$^\circ$) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings from human-centered quantitative assessments of these explanations. Our model is trained using hourly full-disk line-of-sight magnetogram images to predict $\geq$M-class solar flares within the subsequent 24-hour prediction window. Additionally, we apply the Guided Gradient-weighted Class Activation Mapping (Guided Grad-CAM) attribution method to interpret our model's predictions and evaluate the explanations. Our analysis unveils that full-disk solar flare predictions correspond with active region characteristics. The following points represent the most important findings of our study: (1) Our deep learning models achieved an average true skill statistic (TSS) of $\sim$0.51 and a Heidke skill score (HSS) of $\sim$0.38, exhibiting skill to predict solar flares where for central locations the average recall is $\sim$0.75 (recall values for X- and M-class are 0.95 and 0.73 respectively) and for the near-limb flares the average recall is $\sim$0.52 (recall values for X- and M-class are 0.74 and 0.50 respectively); (2) qualitative examination of the model's explanations reveals that it discerns and leverages features linked to active regions in both central and near-limb locations within full-disk magnetograms to produce respective predictions. In essence, our models grasp the shape and texture-based properties of flaring active regions, even in proximity to limb areas -- a novel and essential capability with considerable significance for operational forecasting systems.
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Authors: Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal
Date: 2023-08-30 00:00:00
Tasks: Data Valuation

Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research. However, data valuation faces significant yet frequently overlooked privacy challenges despite its importance. This paper studies these challenges with a focus on KNN-Shapley, one of the most practical data valuation methods nowadays. We first emphasize the inherent privacy risks of KNN-Shapley, and demonstrate the significant technical difficulties in adapting KNN-Shapley to accommodate differential privacy (DP). To overcome these challenges, we introduce TKNN-Shapley, a refined variant of KNN-Shapley that is privacy-friendly, allowing for straightforward modifications to incorporate DP guarantee (DP-TKNN-Shapley). We show that DP-TKNN-Shapley has several advantages and offers a superior privacy-utility tradeoff compared to naively privatized KNN-Shapley in discerning data quality. Moreover, even non-private TKNN-Shapley achieves comparable performance as KNN-Shapley. Overall, our findings suggest that TKNN-Shapley is a promising alternative to KNN-Shapley, particularly for real-world applications involving sensitive data.
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
Authors: Jun Li, Jingjian Wang, Hongwei Wang, Xing Deng, Jielong Chen, Bing Cao, Zekun Wang, Guanjie Xu, Ge Zhang, Feng Shi, Hualei Liu
Date: 2023-08-30 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

Spatial-temporal information has been proven to be of great significance for click-through rate prediction tasks in online Location-Based Services (LBS), especially in mainstream food ordering platforms such as DoorDash, Uber Eats, Meituan, and Ele.me. Modeling user spatial-temporal preferences with sequential behavior data has become a hot topic in recommendation systems and online advertising. However, most of existing methods either lack the representation of rich spatial-temporal information or only handle user behaviors with limited length, e.g. 100. In this paper, we tackle these problems by designing a new spatial-temporal modeling paradigm named Fragment and Integrate Network (FIN). FIN consists of two networks: (i) Fragment Network (FN) extracts Multiple Sub-Sequences (MSS) from lifelong sequential behavior data, and captures the specific spatial-temporal representation by modeling each MSS respectively. Here both a simplified attention and a complicated attention are adopted to balance the performance gain and resource consumption. (ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention. Both public datasets and production datasets have demonstrated the accuracy and scalability of FIN. Since 2022, FIN has been fully deployed in the recommendation advertising system of Ele.me, one of the most popular online food ordering platforms in China, obtaining 5.7% improvement on Click-Through Rate (CTR) and 7.3% increase on Revenue Per Mille (RPM).
Segmenting mechanically heterogeneous domains via unsupervised learning
Authors: Quan Nguyen, Emma Lejeune
Date: 2023-08-30 00:00:00

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work towards developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clutering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code is published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.