A Vector Symbolic Approach to Multiple Instance Learning

• URL: http://arxiv.org/abs/2511.16795v1
• Date: Thu, 20 Nov 2025 20:48:02 GMT
• Title: A Vector Symbolic Approach to Multiple Instance Learning
• Authors: Ehsan Ahmed Dhrubo, Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt,
• Abstract summary: Multiple Instance Learning tasks impose a strict logical constraint: a bag is labeled positive if and only if at least one instance within it is positive.<n>Recent work has shown that most deep learning-based MIL approaches violate it, leading to inflated performance metrics and poor generalization.<n>We propose a novel MIL framework based on Vector Architectures (VSAs), which provide a differentiable mechanism for performing symbolic operations in high-dimensional space.
• Score: 39.59730199795415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple Instance Learning (MIL) tasks impose a strict logical constraint: a bag is labeled positive if and only if at least one instance within it is positive. While this iff constraint aligns with many real-world applications, recent work has shown that most deep learning-based MIL approaches violate it, leading to inflated performance metrics and poor generalization. We propose a novel MIL framework based on Vector Symbolic Architectures (VSAs), which provide a differentiable mechanism for performing symbolic operations in high-dimensional space. Our method encodes the MIL assumption directly into the model's structure by representing instances and concepts as nearly orthogonal high-dimensional vectors and using algebraic operations to enforce the iff constraint during classification. To bridge the gap between raw data and VSA representations, we design a learned encoder that transforms input instances into VSA-compatible vectors while preserving key distributional properties. Our approach, which includes a VSA-driven MaxNetwork classifier, achieves state-of-the-art results for a valid MIL model on standard MIL benchmarks and medical imaging datasets, outperforming existing methods while maintaining strict adherence to the MIL formulation. This work offers a principled, interpretable, and effective alternative to existing MIL approaches that rely on learned heuristics.

Related papers

• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images [12.827931905880163]
  This paper proposes to pre-train feature extractor for MIL via a weakly-supervised scheme.<n>To learn effective features for MIL, we delve into several key components, including strong data augmentation, a non-linear prediction head and the robust loss function.<n>We conduct experiments on common large-scale WSI datasets and find it achieves better performance than other pre-training schemes.
  arXiv  Detail & Related papers  (2025-05-10T17:23:36Z)
• A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology [4.012490059423154]
  Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images.<n>Recent advancements, such as Transformer based MIL (TransMIL), have incorporated spatial context and inter-patch relationships.<n>In this work, we enhance the ABMIL framework by integrating interaction-aware representations to address this question.
  arXiv  Detail & Related papers  (2025-04-24T08:53:46Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation [61.64052577026623]
  Real-world multi-view datasets are often heterogeneous and imperfect.<n>We propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment.<n>Our RML is self-supervised and can also be applied for downstream tasks as a regularization.
  arXiv  Detail & Related papers  (2025-03-06T07:01:08Z)
• MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models [56.37780601189795]
  We propose a framework named MamMIL for WSI analysis. We represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we propose a topology-aware scanning mechanism.
  arXiv  Detail & Related papers  (2024-03-08T09:02:13Z)
• PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers [2.069061136213899]
  Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. We present a novel approach in the form of a Progressive Dropout Layer (PDL) to address overfitting and empower the MIL model in uncovering intricate and impactful feature representations.
  arXiv  Detail & Related papers  (2023-08-19T21:20:30Z)
• Feature Re-calibration based MIL for Whole Slide Image Classification [7.92885032436243]
  Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases. We propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder.
  arXiv  Detail & Related papers  (2022-06-22T07:00:39Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• How Fine-Tuning Allows for Effective Meta-Learning [50.17896588738377]
  We present a theoretical framework for analyzing representations derived from a MAML-like algorithm. We provide risk bounds on the best predictor found by fine-tuning via gradient descent, demonstrating that the algorithm can provably leverage the shared structure. This separation result underscores the benefit of fine-tuning-based methods, such as MAML, over methods with "frozen representation" objectives in few-shot learning.
  arXiv  Detail & Related papers  (2021-05-05T17:56:00Z)
• Dual-stream Maximum Self-attention Multi-instance Learning [11.685285490589981]
  Multi-instance learning (MIL) is a form of weakly supervised learning where a single class label is assigned to a bag of instances while the instance-level labels are not available. We propose a dual-stream maximum self-attention MIL model (DSMIL) parameterized by neural networks. Our method achieves superior performance compared to the best MIL methods and demonstrates state-of-the-art performance on benchmark MIL datasets.
  arXiv  Detail & Related papers  (2020-06-09T22:44:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.