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University of Massachusetts Amherst

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Research library639linked papers
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Selected work

Representative Papers

Latent Target Score Matching, with an application to Simulation-Based Inference

Feb 06, 2026

This work addresses the challenge of score estimation in the presence of latent variables, where conventional denoising score matching (DSM) suffers from high variance at low noise levels, and target score matching (TSM) is inapplicable due to the unavailability of clean-data scores. To overcome this limitation, the authors propose Latent-variable Target Score Matching (LTSM), which extends TSM to settings with latent variables for the first time. LTSM leverages the score of the joint distribution to provide low-variance supervision for the marginal score and integrates DSM into a hybrid training strategy that ensures robustness across varying noise scales. Experimental results demonstrate that LTSM substantially reduces estimation variance, leading to improved score estimation accuracy and enhanced generative sample quality.

2 citations1 influentialRead paper

The iNaturalist Sounds Dataset

May 31, 2025Neural Information Processing Systems

To address the scarcity of bioacoustic data, high annotation costs, and insufficient cross-taxa coverage, this study introduces iNatSound—the first large-scale, multi-taxon (birds, mammals, insects, etc.), weakly supervised global bioacoustic dataset, comprising 230,000 audio recordings from over 5,500 species, sourced from iNaturalist citizen science observations. Innovatively integrating field-collected weakly labeled audio, iNatSound supports both single-species classification and multi-label learning. A rigorous cross-dataset evaluation protocol is designed to validate its utility as a pretraining resource for downstream strongly labeled tasks. Leveraging contrastive learning with multiple backbone architectures (e.g., ResNet, EfficientNet), models pretrained on iNatSound achieve significant performance gains across multiple acoustic recognition benchmarks. The dataset is publicly released, establishing a foundational resource for ecological AI and participatory biodiversity monitoring.

2 citations1 influentialRead paper

Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization

Feb 15, 2026

This work addresses the challenge in self-supervised learning that high-dimensional representations are difficult to explicitly maximize in terms of mutual information, and existing methods often fail to fully achieve maximum entropy. To this end, the authors propose a radial Gaussianization loss that aligns the feature norms with a chi-squared distribution, thereby expanding the class of feature distributions amenable to transformation into a standard normal distribution. This approach effectively attenuates higher-order dependencies and enhances representation diversity. Integrated into the VCReg framework, the method optimizes the statistical properties of features to more comprehensively approximate a high-dimensional Gaussian distribution. Experiments demonstrate significant improvements in both the informativeness and discriminability of learned representations on both synthetic and real-world datasets.

1 citationsRead paper

GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health

Jan 26, 2026

This work addresses critical limitations in existing personalized dietary guidance approaches—namely, their frequent neglect of real-world constraints, insufficient interpretability, and lack of a unified evaluation benchmark. To bridge this gap, the authors introduce the first graph–language integrated benchmark for nutritional health, which synthesizes multimodal real-world data including health records, food composition, and accessibility. They construct a knowledge graph linking demographics, medical conditions, dietary behaviors, and resource constraints, and propose a unified evaluation framework centered on three core tasks: risk identification, personalized recommendation, and natural language question answering. Leveraging a hybrid architecture combining graph neural networks and large language models, the approach enables resource-aware, interpretable nutritional interventions. Experiments not only uncover dietary patterns significantly associated with health risks but also yield actionable insights for practical deployment and establish a robust baseline for future research.

1 citationsRead paper
Recent publications

Latest Papers

Parameter-Free Triangle Counting

Sep 21, 2026

本文提出了一种无参数的流式三角计数算法，解决了在不知道三角形数量T的情况下，如何在亚线性空间内近似计算图中的三角形数量问题。

0 citationsRead paper