Résultats de recherche pour « Vector embeddings »

This paper proposes a model to learn word embeddings with weighted contexts based on part-of-speech (POS) relevance weights. POS is a fundamental element in natural language. However, state-of-the-art word embedding models fail to consider it. This paper proposes to use position-dependent POS relevance weighting matrices to model the inherent syntactic relationship among words within a context window. We utilize the POS relevance weights to model each word-context pairs during the word embedding training process. The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices. Experiments conducted on popular word analogy and word similarity tasks all demonstrated the effectiveness of the proposed method.

Stock embeddings learned from correlation networks or return series are increasingly used as compact descriptors of asset similarity. We ask a simple question: do these stock embeddings outperform or tie with the raw features they compress? We benchmark a modular pipeline (relationship matrix, graph construction, embedding generation, evaluation) across three universes (S&P 500, Russell 1000, Russell 3000), four horizons, six co-movement-removal methods, five graph configurations, and six representation families-including a faithful reproduction of Sarmah et. al.-evaluating retrieval (hit@k, success@k, MRR), clustering (V-measure, NMI, ARI), and supervised readout (SVM-F1) against GICS labels, with paired Wilcoxon and McNemar tests, bootstrap intervals, and Benjamini-Hochberg correction. The answer is baseline-dependent. Every learned family decisively beats a leak-free raw-correlation-row baseline (+0.11 to +0.21 in sub-industry hit@3, p ≪ 0.001), but none clearly beats the raw return series: at Russell 3000 the best embedding is statistically tied with raw returns (p = 0.69), and elsewhere raw returns leads by at most 0.027-0.029 in hit@3. Under a decision rule requiring BH-significance plus one percentage point, raw returns never loses. Embeddings' durable advantage is compression: 16-64 dimensions match thousands of raw features. An out-of-sample peer-hedging experiment shows no risk-reduction advantage over a direct Pearson hedge. All results use full-survivor panels and compare methods under idealised conditions, not live trading.

In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning. We propose an indexing pipeline in which these techniques are applied before clustering, and we focus on how they affect storage footprint, clustering time, and the quality of the resulting centroids for vector search tasks. Our results reveal that using full-precision vectors for clustering is excessive, as even 1-bit codes can achieve near-optimal clustering quality (within 1% of ideal) while reducing storage requirements by 60x and delivering attractive performance gains (Figure 1). We open-source our implementations at https://github.com/cwida/SuperKMeans.

Platform kursus digital masih menghadapi tingkat penyelesaian belajar yang rendah karena pengguna sering kesulitan menentukan urutan materi yang sesuai dengan tujuan, minat, dan kemampuan awal. Personalisasi jalur belajar menjadi pendekatan yang relevan, namun menghadapi kendala cold-start problem ketika riwayat interaksi pengguna belum tersedia. Penelitian ini mengimplementasikan fitur rekomendasi jalur belajar berbasis semantic search menggunakan model text-embedding-3-small dari OpenAI pada platform kursus digital berbasis web bernama Genii. Setiap kursus direpresentasikan dari judul, deskripsi, topik, tingkat kesulitan, dan ringkasan lesson, lalu dikonversi menjadi embeddingberdimensi 1536 dan diindeks pada layanan vector search Convex. Rekomendasi dihasilkan dengan membentuk embedding preferensi pengguna, menghitung kemiripan menggunakan cosine similarity, dan mengambil Top-K kursus dengan skor tertinggi. Evaluasi penerimaan pengguna dilakukan menggunakan Technology Acceptance Model (TAM) terhadap 50 responden dari 14 provinsi. Hasil menunjukkan nilai rata-rata Perceived Usefulness (PU) 4,40, Perceived Ease of Use (PEOU) 4,33, dan Behavioral Intention (BI) 4,38 pada skala 1–5, dengan reliabilitas konstruk Cronbach’s alpha di atas 0,70. Korelasi Pearson menunjukkan hubungan positif signifikan antara PU dan BI (r = 0,7324, p < 0,001) serta PEOU dan BI (r = 0,6847, p < 0,001). Penelitian ini berkontribusi pada pembuktian bahwa pendekatan semantic search berbasis embedding dapat diimplementasikan secara realistis pada platform pendidikan digital dan dipersepsikan bermanfaat serta mudah digunakan oleh pengguna.

Inequivalent standard-like observable sector embeddings in $Z_3$ orbifolds with two discrete Wilson lines, as determined by Casas, Mondragon and Muñoz, are completed by examining all possible ways of embedding the hidden sector. The hidden sector embeddings are relevant to twisted matter in nontrivial representations of the Standard Model and to scenarios where supersymmetry breaking is generated in a hidden sector. We find a set of 175 models which have a hidden sector gauge group which is viable for dynamical supersymmetry breaking. Only four different hidden sector gauge groups are possible in these models.

Bitcoin Blockchain is a completely public open currency transaction ledger, recent growth in the crypto-currency market has driven many lawful and blackmarket actors using Bitcoin as the main method of payment. I propose address2vec an algorithm to generate vector representations of addresses on the Bitcoin Blockchain.I am able to present better results than a baseline random approach in predicting hoarding vs spending beaviour. The current work allows for utilization of common machine learning algorithms on bitcoin transaction addresses.

A vector balleans is a vector space over $\mathbb{R}$ endowed with a coarse structure in such a way that the vector operations are coarse mappings. We prove that, for every ballean $(X, \mathcal{E})$, there exists the unique free vector ballean $\mathbb{V}(X, \mathcal{E})$ and describe the coarse structure of $\mathbb{V}(X, \mathcal{E})$. It is shown that normality of $\mathbb{V}(X, \mathcal{E})$ is equivalent to metrizability of $(X, \mathcal{E})$.

Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods for inspecting embeddings usually rely on visualization methods, which do not work on a larger scale and do not give concrete interpretations of vector embeddings in terms of preserved network properties (e.g., centrality or betweenness measures). In this paper, we study and investigate network properties preserved by recent random walk-based embedding procedures like node2vec, DeepWalk or LINE. We propose a method that applies learning to rank in order to relate embeddings to network centralities. We evaluate our approach with extensive experiments on real-world and artificial social networks. Experiments show that each embedding method learns different network properties. In addition, we show that our graph embeddings in combination with neural networks provide a computationally efficient way to approximate the Closeness Centrality measure in social networks.

A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (eg. distributions, complex or hyperbolic vectors, or regions such as cones, disks, or boxes) have been explored for their alternative inductive biases and additional representational capacities. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings.

<div>We develop and evaluate an automated data-driven framework for providing reviewer recommendations for submitted manuscripts. Given inputs comprising a set of manuscripts for review and a listing of a pool of prospective reviewers, our system uses a publisher database to extract papers authored by the reviewers from which a Paragraph Vector (doc2vec ) neural network model is learned and used to obtain vector space embeddings of documents. Similarities between embeddings of an individual reviewer’s papers and a manuscript are then used to compute manuscript-reviewer match scores and to generate a ranked list of recommended reviewers for each manuscript. Our mainline proposed system uses full text versions of the reviewers’ papers, which we demonstrate performs significantly better than models developed based on abstracts alone, which has been the predominant paradigm in prior work. Direct retrieval of reviewer’s manuscripts from a publisher database reduces reviewer burden, ensures up-to-date data, and eliminates the potential for misuse through data manipulation. We also propose a useful evaluation methodology that addresses hyperparameter selection and enables indirect comparisons with alternative approaches and on prior datasets. Finally, the work also contributes a large scale retrospective reviewer matching dataset and evaluation that we hope will be useful for further research in this field. Our system is quite effective; for the mainline approach, expert judges rated 38% of the recommendations as Very Relevant; 33% as Relevant; 24% as Slightly Relevant; and only 5% as Irrelevant.</div>