Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information

• URL: http://arxiv.org/abs/2309.11294v1
• Date: Wed, 20 Sep 2023 13:21:12 GMT
• Title: Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
• Authors: Sarwan Ali
• Abstract summary: We propose a method to measure the textitrepresentation capacity of embeddings. The motivation behind this work stems from the importance of understanding the strengths and limitations of embeddings. The proposed method not only contributes to advancing the field of embedding evaluation but also empowers researchers and practitioners with a quantitative measure.
• Score: 1.8130068086063336
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Effective representation of data is crucial in various machine learning tasks, as it captures the underlying structure and context of the data. Embeddings have emerged as a powerful technique for data representation, but evaluating their quality and capacity to preserve structural and contextual information remains a challenge. In this paper, we address this need by proposing a method to measure the \textit{representation capacity} of embeddings. The motivation behind this work stems from the importance of understanding the strengths and limitations of embeddings, enabling researchers and practitioners to make informed decisions in selecting appropriate embedding models for their specific applications. By combining extrinsic evaluation methods, such as classification and clustering, with t-SNE-based neighborhood analysis, such as neighborhood agreement and trustworthiness, we provide a comprehensive assessment of the representation capacity. Additionally, the use of optimization techniques (bayesian optimization) for weight optimization (for classification, clustering, neighborhood agreement, and trustworthiness) ensures an objective and data-driven approach in selecting the optimal combination of metrics. The proposed method not only contributes to advancing the field of embedding evaluation but also empowers researchers and practitioners with a quantitative measure to assess the effectiveness of embeddings in capturing structural and contextual information. For the evaluation, we use $3$ real-world biological sequence (proteins and nucleotide) datasets and performed representation capacity analysis of $4$ embedding methods from the literature, namely Spike2Vec, Spaced $k$-mers, PWM2Vec, and AutoEncoder.

Related papers

• Benchmarking pre-trained text embedding models in aligning built asset information [0.0]
  This study presents a comparative benchmark of state-of-the-art text embedding models to evaluate their effectiveness in aligning built asset information with domain-specific technical concepts. The results of our benchmarking across six proposed datasets, covering three tasks of clustering, retrieval, and reranking, highlight the need for future research on domain adaptation techniques.
  arXiv  Detail & Related papers  (2024-11-18T20:54:17Z)
• Exploring Information Retrieval Landscapes: An Investigation of a Novel Evaluation Techniques and Comparative Document Splitting Methods [0.0]
  In this study, the structured nature of textbooks, the conciseness of articles, and the narrative complexity of novels are shown to require distinct retrieval strategies. A novel evaluation technique is introduced, utilizing an open-source model to generate a comprehensive dataset of question-and-answer pairs. The evaluation employs weighted scoring metrics, including SequenceMatcher, BLEU, METEOR, and BERT Score, to assess the system's accuracy and relevance.
  arXiv  Detail & Related papers  (2024-09-13T02:08:47Z)
• Value Alignment from Unstructured Text [32.9140028463247]
  We introduce a systematic end-to-end methodology for aligning large language models (LLMs) to the implicit and explicit values represented in unstructured text data. Our proposed approach leverages the use of scalable synthetic data generation techniques to effectively align the model to the values present in the unstructured data. Our approach credibly aligns LLMs to the values embedded within documents, and shows improved performance against other approaches.
  arXiv  Detail & Related papers  (2024-08-19T20:22:08Z)
• Measuring What Matters: Intrinsic Distance Preservation as a Robust Metric for Embedding Quality [0.0]
  This paper introduces the Intrinsic Distance Preservation Evaluation (IDPE) method for assessing embedding quality. IDPE is based on the preservation of Mahalanobis distances between data points in the original and embedded spaces. Our results show that IDPE offers a more comprehensive and reliable assessment of embedding quality across various scenarios.
  arXiv  Detail & Related papers  (2024-07-31T13:26:09Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective [64.73162159837956]
  evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset. Our empirical analysis of DataCOPE in the logged contextual bandit settings using healthcare datasets confirms its ability to evaluate both machine-learning and human expert policies.
  arXiv  Detail & Related papers  (2023-11-23T17:13:37Z)
• CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation [67.12857074801731]
  We introduce a novel method, CPPF++, designed for sim-to-real pose estimation. To address the challenge posed by vote collision, we propose a novel approach that involves modeling the voting uncertainty. We incorporate several innovative modules, including noisy pair filtering, online alignment optimization, and a feature ensemble.
  arXiv  Detail & Related papers  (2022-11-24T03:27:00Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Top-K Ranking Deep Contextual Bandits for Information Selection Systems [0.0]
  We propose a novel approach to top-K rankings under the contextual multi-armed bandit framework. We model the reward function with a neural network to allow non-linear approximation to learn the relationship between rewards and contexts.
  arXiv  Detail & Related papers  (2022-01-28T15:10:44Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)

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.