pwc-archive/papers-with-abstracts huggingface dataset:
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Authors: SeungHyun Lee, Yuqi Gu
Date: 2025-01-02 00:00:00
Tasks: parameter estimation, Representation Learning

In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose an interpretable deep generative modeling framework for rich data types with discrete latent layers, called Deep Discrete Encoders (DDEs). A DDE is a directed graphical model with multiple binary latent layers. Theoretically, we propose transparent identifiability conditions for DDEs, which imply progressively smaller sizes of the latent layers as they go deeper. Identifiability ensures consistent parameter estimation and inspires an interpretable design of the deep architecture. Computationally, we propose a scalable estimation pipeline of a layerwise nonlinear spectral initialization followed by a penalized stochastic approximation EM algorithm. This procedure can efficiently estimate models with exponentially many latent components. Extensive simulation studies validate our theoretical results and demonstrate the proposed algorithms' excellent performance. We apply DDEs to three diverse real datasets for hierarchical topic modeling, image representation learning, response time modeling in educational testing, and obtain interpretable findings.
A Multi-task Supervised Compression Model for Split Computing
Authors: Yoshitomo Matsubara, Matteo Mendula, Marco Levorato
Date: 2025-01-02 00:00:00
Tasks: Edge-computing, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

Split computing ($\neq$ split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through channels with limited communication capacity. State-of-theart work on split computing presents methods for single tasks such as image classification, object detection, or semantic segmentation. The application of existing methods to multitask problems degrades model accuracy and/or significantly increase runtime latency. In this study, we propose Ladon, the first multi-task-head supervised compression model for multi-task split computing. Experimental results show that the multi-task supervised compression model either outperformed or rivaled strong lightweight baseline models in terms of predictive performance for ILSVRC 2012, COCO 2017, and PASCAL VOC 2012 datasets while learning compressed representations at its early layers. Furthermore, our models reduced end-to-end latency (by up to 95.4%) and energy consumption of mobile devices (by up to 88.2%) in multi-task split computing scenarios.
Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models
Authors: Jingfeng Yao, Xinggang Wang
Date: 2025-01-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation

Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: while increasing the per-token feature dimension in visual tokenizers improves reconstruction quality, it requires substantially larger diffusion models and more training iterations to achieve comparable generation performance. Consequently, existing systems often settle for sub-optimal solutions, either producing visual artifacts due to information loss within tokenizers or failing to converge fully due to expensive computation costs. We argue that this dilemma stems from the inherent difficulty in learning unconstrained high-dimensional latent spaces. To address this, we propose aligning the latent space with pre-trained vision foundation models when training the visual tokenizers. Our proposed VA-VAE (Vision foundation model Aligned Variational AutoEncoder) significantly expands the reconstruction-generation frontier of latent diffusion models, enabling faster convergence of Diffusion Transformers (DiT) in high-dimensional latent spaces. To exploit the full potential of VA-VAE, we build an enhanced DiT baseline with improved training strategies and architecture designs, termed LightningDiT. The integrated system achieves state-of-the-art (SOTA) performance on ImageNet 256x256 generation with an FID score of 1.35 while demonstrating remarkable training efficiency by reaching an FID score of 2.11 in just 64 epochs--representing an over 21 times convergence speedup compared to the original DiT. Models and codes are available at: https://github.com/hustvl/LightningDiT.
Unifying Specialized Visual Encoders for Video Language Models
Authors: Jihoon Chung, Tyler Zhu, Max Gonzalez Saez-Diez, Juan Carlos Niebles, Honglu Zhou, Olga Russakovsky
Date: 2025-01-02 00:00:00
Tasks: Multiple-choice, Video Understanding

The recent advent of Large Language Models (LLMs) has ushered sophisticated reasoning capabilities into the realm of video through Video Large Language Models (VideoLLMs). However, VideoLLMs currently rely on a single vision encoder for all of their visual processing, which limits the amount and type of visual information that can be conveyed to the LLM. Our method, MERV, Multi-Encoder Representation of Videos, instead leverages multiple frozen visual encoders to create a unified representation of a video, providing the VideoLLM with a comprehensive set of specialized visual knowledge. Spatio-temporally aligning the features from each encoder allows us to tackle a wider range of open-ended and multiple-choice video understanding questions and outperform prior state-of-the-art works. MERV is up to 3.7% better in accuracy than Video-LLaVA across the standard suite video understanding benchmarks, while also having a better Video-ChatGPT score. We also improve upon SeViLA, the previous best on zero-shot Perception Test accuracy, by 2.2%. MERV introduces minimal extra parameters and trains faster than equivalent single-encoder methods while parallelizing the visual processing. Finally, we provide qualitative evidence that MERV successfully captures domain knowledge from each of its encoders. Our results offer promising directions in utilizing multiple vision encoders for comprehensive video understanding.
Ensuring superior learning outcomes and data security for authorized learner
Authors: Jeongho Bang, Wooyeong Song, Kyujin Shin, Yong-Su Kim
Date: 2025-01-01 00:00:00
Tasks: image-classification, Image Classification, PAC learning

The learner's ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks. Thus, it is important to ensure the performance of the "authorized" learner by limiting the quality of the training data accessible to eavesdroppers. Unlike previous studies focusing on encryption or access controls, we provide a theorem to ensure superior learning outcomes exclusively for the authorized learner with quantum label encoding. In this context, we use the probably-approximately-correct (PAC) learning framework and introduce the concept of learning probability to quantitatively assess learner performance. Our theorem allows the condition that, given a training dataset, an authorized learner is guaranteed to achieve a certain quality of learning outcome, while eavesdroppers are not. Notably, this condition can be constructed based only on the authorized-learning-only measurable quantities of the training data, i.e., its size and noise degree. We validate our theoretical proofs and predictions through convolutional neural networks (CNNs) image classification learning.
Adventures in Demand Analysis Using AI
Authors: Philipp Bach, Victor Chernozhukov, Sven Klaassen, Martin Spindler, Jan Teichert-Kluge, Suhas Vijaykumar
Date: 2024-12-31 00:00:00
Tasks: Causal Inference

This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on \textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly.
Alternating minimization for square root principal component pursuit
Authors: Shengxiang Deng, Xudong Li, Yangjing Zhang
Date: 2024-12-31 00:00:00

Recently, the square root principal component pursuit (SRPCP) model has garnered significant research interest. It is shown in the literature that the SRPCP model guarantees robust matrix recovery with a universal, constant penalty parameter. While its statistical advantages are well-documented, the computational aspects from an optimization perspective remain largely unexplored. In this paper, we focus on developing efficient optimization algorithms for solving the SRPCP problem. Specifically, we propose a tuning-free alternating minimization (AltMin) algorithm, where each iteration involves subproblems enjoying closed-form optimal solutions. Additionally, we introduce techniques based on the variational formulation of the nuclear norm and Burer-Monteiro decomposition to further accelerate the AltMin method. Extensive numerical experiments confirm the efficiency and robustness of our algorithms.
Optimal Sampling for Generalized Linear Model under Measurement Constraint with Surrogate Variables
Authors: Yixin Shen, Yang Ning
Date: 2025-01-01 00:00:00
Tasks: Experimental Design

Measurement-constrained datasets, often encountered in semi-supervised learning, arise when data labeling is costly, time-intensive, or hindered by confidentiality or ethical concerns, resulting in a scarcity of labeled data. In certain cases, surrogate variables are accessible across the entire dataset and can serve as approximations to the true response variable; however, these surrogates often contain measurement errors and thus cannot be directly used for accurate prediction. We propose an optimal sampling strategy that effectively harnesses the available information from surrogate variables. This approach provides consistent estimators under the assumption of a generalized linear model, achieving theoretically lower asymptotic variance than existing optimal sampling algorithms that do not use surrogate data information. By employing the A-optimality criterion from optimal experimental design, our strategy maximizes statistical efficiency. Numerical studies demonstrate that our approach surpasses existing optimal sampling methods, exhibiting reduced empirical mean squared error and enhanced robustness in algorithmic performance. These findings highlight the practical advantages of our strategy in scenarios where measurement constraints exist and surrogates are available.
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals
Authors: E. Curbelo, L. Martino, F. Llorente, D. Delgado-Gomez
Date: 2025-01-02 00:00:00
Tasks: Bayesian Inference

In this paper we address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model and the covariance matrix of the different output signals. We propose an adaptive importance sampling (AIS) scheme for multivariate Bayesian inversion problems, which is based in two main ideas: the variables of interest are split in two blocks and the inference takes advantage of known analytical optimization formulas. We estimate both the unknown parameters of the multivariate non-linear model and the covariance matrix of the noise. In the first part of the proposed inference scheme, a novel AIS technique called adaptive target adaptive importance sampling (ATAIS) is designed, which alternates iteratively between an IS technique over the parameters of the non-linear model and a frequentist approach for the covariance matrix of the noise. In the second part of the proposed inference scheme, a prior density over the covariance matrix is considered and the cloud of samples obtained by ATAIS are recycled and re-weighted to obtain a complete Bayesian study over the model parameters and covariance matrix. ATAIS is the main contribution of the work. Additionally, the inverted layered importance sampling (ILIS) is presented as a possible compelling algorithm (but based on a conceptually simpler idea). Different numerical examples show the benefits of the proposed approaches
Fast data inversion for high-dimensional dynamical systems from noisy measurements
Authors: Yizi Lin, Xubo Liu, Paul Segall, Mengyang Gu
Date: 2025-01-02 00:00:00
Tasks: parameter estimation

In this work, we develop a scalable approach for a flexible latent factor model for high-dimensional dynamical systems. Each latent factor process has its own correlation and variance parameters, and the orthogonal factor loading matrix can be either fixed or estimated. We utilize an orthogonal factor loading matrix that avoids computing the inversion of the posterior covariance matrix at each time of the Kalman filter, and derive closed-form expressions in an expectation-maximization algorithm for parameter estimation, which substantially reduces the computational complexity without approximation. Our study is motivated by inversely estimating slow slip events from geodetic data, such as continuous GPS measurements. Extensive simulated studies illustrate higher accuracy and scalability of our approach compared to alternatives. By applying our method to geodetic measurements in the Cascadia region, our estimated slip better agrees with independently measured seismic data of tremor events. The substantial acceleration from our method enables the use of massive noisy data for geological hazard quantification and other applications.
NewsHomepages: Homepage Layouts Capture Information Prioritization Decisions
Authors: Ben Welsh, Naitian Zhou, Arda Kaz, Michael Vu, Alexander Spangher
Date: 2024-11-21 00:00:00

Information prioritization plays an important role in how humans perceive and understand the world. Homepage layouts serve as a tangible proxy for this prioritization. In this work, we present NewsHomepages, a large dataset of over 3,000 new website homepages (including local, national and topic-specific outlets) captured twice daily over a three-year period. We develop models to perform pairwise comparisons between news items to infer their relative significance. To illustrate that modeling organizational hierarchies has broader implications, we applied our models to rank-order a collection of local city council policies passed over a ten-year period in San Francisco, assessing their "newsworthiness". Our findings lay the groundwork for leveraging implicit organizational cues to deepen our understanding of information prioritization.
Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models
Authors: Yifan Tian, Yixin Liu, Yi Bu, Jiqun Liu
Date: 2024-12-31 00:00:00

This paper investigates the performance of several representative large models in the tasks of literature recommendation and explores potential biases in research exposure. The results indicate that not only LLMs' overall recommendation accuracy remains limited but also the models tend to recommend literature with greater citation counts, later publication date, and larger author teams. Yet, in scholar recommendation tasks, there is no evidence that LLMs disproportionately recommend male, white, or developed-country authors, contrasting with patterns of known human biases.
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain
Authors: Rui Xia, Yanhua Cheng, Yongxiang Tang, Xiaocheng Liu, Xialong Liu, Lisong Wang, Peng Jiang
Date: 2024-12-31 00:00:00
Tasks: Collaborative Filtering, Denoising, Recommendation Systems

Recovering user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent distributions, they often fail to capture similar users' collective preferences effectively. Additionally, latent variables degrade into pure Gaussian noise during the forward process, lowering the signal-to-noise ratio, which in turn degrades performance. To address this, we propose S-Diff, inspired by graph-based collaborative filtering, better to utilize low-frequency components in the graph spectral domain. S-Diff maps user interaction vectors into the spectral domain and parameterizes diffusion noise to align with graph frequency. This anisotropic diffusion retains significant low-frequency components, preserving a high signal-to-noise ratio. S-Diff further employs a conditional denoising network to encode user interactions, recovering true preferences from noisy data. This method achieves strong results across multiple datasets.
Search Plurality
Authors: Shiran Dudy
Date: 2025-01-02 00:00:00

In light of Phillips' contention regarding the impracticality of Search Neutrality, asserting that non-epistemic factors presently dictate result prioritization, our objective in this study is to confront this constraint by questioning prevailing design practices in search engines. We posit that the concept of prioritization warrants scrutiny, along with the consistent hierarchical ordering that underlies this lack of neutrality. We introduce the term Search Plurality to encapsulate the idea of emphasizing the various means a query can be approached. This is demonstrated in a design that prioritizes the display of categories over specific search items, helping users grasp the breadth of their search. Whether a query allows for multiple interpretations or invites diverse opinions, the presentation of categories highlights the significance of organizing data based on relevance, importance, and relative significance, akin to traditional methods. However, unlike previous approaches, this method enriches our comprehension of the overall information landscape, countering the potential bias introduced by ranked lists.
Crime Hotspot Analysis and Mapping Using Geospatial Technology in Dessie City, Ethiopia
Authors: H. A. Kebede, M. M. Assen, M. A. Sharew
Date: 2024-12-24 00:00:00

Over the past few decades, crime and delinquency rates have increased drastically in many countries; nevertheless, it is important to note that crime trends can differ significantly by geographic region. This study's primary goal was to use geographic technology to map and analyze Dessie City's crime patterns. To investigate the geographic clustering of crime, the researchers used semivariogram modeling and spatial autocorrelation analysis with Moran'sI. The neighborhoods of Hote, Arada, and Segno in Dessie's central city were found to be crime-prone "hot spot" locations, as evidenced by statistically significant high Z-scores ranging from 0.037 to 4.608. On the other hand, low negative Z-scores ranging from -3.231 to -0.116 indicated "cold spot" concentrations of crime in the city's north-central sub-cities of Menafesha and Bounbouwha. With an index of 0.027492 and a Z-score of 3.297616 (p<0.01), the analysis overall showed a substantial positive spatial autocorrelation, suggesting a clustered pattern of crime in Dessie. The majority of crimes showed a north-south directionality, except for murder, which trended from northeast to southwest. The mean center of all crime types was found in the central Hote area. To address the complicated problem of rising crime rates in Dessie and other developing metropolitan areas, more focused and efficient enforcement techniques, and resource deployment can be informed through the knowledge acquired from the geospatial analysis.
The Text Classification Pipeline: Starting Shallow going Deeper
Authors: Marco Siino, Ilenia Tinnirello, Marco La Cascia
Date: 2024-12-30 00:00:00
Tasks: Classification, Information Retrieval, Retrieval, text-classification, Text Classification, Text Retrieval

Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature includes datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of text classification models relies heavily on their ability to capture intricate textual relationships and non-linear correlations, necessitating a comprehensive examination of the entire text classification pipeline. In the NLP domain, a plethora of text representation techniques and model architectures have emerged, with Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) at the forefront. These models are adept at transforming extensive textual data into meaningful vector representations encapsulating semantic information. The multidisciplinary nature of text classification, encompassing data mining, linguistics, and information retrieval, highlights the importance of collaborative research to advance the field. This work integrates traditional and contemporary text mining methodologies, fostering a holistic understanding of text classification.
Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion
Authors: Hebin Wang, Yangning Li, Yinghui Li, Hai-Tao Zheng, Wenhao Jiang, Hong-Gee Kim
Date: 2024-12-31 00:00:00

The rapid development of multimodal large language models (MLLMs) has brought significant improvements to a wide range of tasks in real-world applications. However, LLMs still exhibit certain limitations in extracting implicit semantic information. In this paper, we apply MLLMs to the Multi-modal Entity Set Expansion (MESE) task, which aims to expand a handful of seed entities with new entities belonging to the same semantic class, and multi-modal information is provided with each entity. We explore the capabilities of MLLMs to understand implicit semantic information at the entity-level granularity through the MESE task, introducing a listwise ranking method LUSAR that maps local scores to global rankings. Our LUSAR demonstrates significant improvements in MLLM's performance on the MESE task, marking the first use of generative MLLM for ESE tasks and extending the applicability of listwise ranking.