A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Introduces CHL, a new loss function for continuous similarity learning.
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small sub…
Deconfounds neural network representation similarity metrics to improve consistency and accuracy.
Quantum networks learn task-dependent asymmetric similarity measures.
In this paper, we investigate the similarity transformations in the Minkowski-n space. We study the geometric invariants of non-null curves under the similarity transformations. Besides, we extend the fundamental theorem for a non-null curve according to a similarity motion. We determine all non-null self-similar curve…
Defines a similarity measure for classification distributions.
The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known for its vulnerability of noise contamination or the imbalance in samples or features, and thus hinders accurate clustering. To tackl…
We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs. As the core operation of graph similarity search, pairwise graph similarity computation is a challenging problem due to the NP-hard nature of computing many graph distance/simila…
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns dis…
Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain exp…
Modified cosine distance improves similarity performance in data with variance and correlation.
Advocates Tversky's model for image similarity learning.
We prove that the only self-similar surfaces of Euclidean 3-space which are foliated by circles are the self-similar surfaces of revolution discovered by S. Angenent and that the only ruled, self-similar surfaces are the cylinders over planar self-similar curves.
Method measures weight similarity in neural networks using normalization and statistical inference.
Improves confidence calibration in neural networks by smoothing labels based on class similarity.
CatSIM measures image similarity robustly to small changes.
Recently, randomly mapping vectorial data to strings of discrete symbols (i.e., sketches) for fast and space-efficient similarity searches has become popular. Such random mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although many efficient similarit…
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
Inner product-based convolution has been the founding stone of convolutional neural networks (CNNs), enabling end-to-end learning of visual representation. By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs. Neural si…
STRAPSim measures ETF portfolio similarity better than existing methods.
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE). In contrast to IPS, that is limited to approximating positive-definite (PD) similarities, SIPS goes beyond the limitation by introd…
This paper proposes novel algorithms for speaker embedding using subjective inter-speaker similarity based on deep neural networks (DNNs). Although conventional DNN-based speaker embedding such as a -vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-…
Exploits class similarity for better machine learning models with confidence labels and projective loss functions.
We propose (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values. Despite its simplicity, WIPS can approximate arbitrary general similarities in…
BiLRP explains deep similarity models by decomposing scores into feature contributions.
The problem of hierarchical clustering items from pairwise similarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limited by the cost of obtaining similarities between pairs of items. While prior work has been developed to reconstruct cl…
Lipschitz equivalence of self-similar sets is an important area in the study of fractal geometry. It is known that two dust-like self-similar sets with the same contraction ratios are always Lipschitz equivalent. However, when self-similar sets have touching structures the problem of Lipschitz equivalence becomes much …
New method improves reinforcement learning generalization.
New findings clarify the link between distributional closeness and representational similarity.
Neural networks auto-denoise similar inputs, enabling new statistical analysis.
Unified understanding of neural representation similarity measures.
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similari…
CLS measures dataset similarity through decision rule performance.
New method for estimating firm linkages using CVLs and QCML.
New measure quantifies function similarity for optimization.
Clustering is an underspecified task: there are no universal criteria for what makes a good clustering. This is especially true for relational data, where similarity can be based on the features of individuals, the relationships between them, or a mix of both. Existing methods for relational clustering have strong and …
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
Language-based methods improve human similarity approximations without requiring many human judgments.
Similarity algebra extends algebraic structures with quantitative bounds.
Classifies self-similar curve shortening flows in hyperbolic 2-space.
Skeleton is a new notion designed for constructing space-filling curves of self-similar sets. It is shown in [Dai, Rao and Zhang, Space-filling curves of self-similar sets (II): Edge-to-trail substitution rule,https://doi.org/10.1088/1361-6544/ab1275] that for a connected self-similar set, space-filling curves can be c…
New insights into continual learning with task similarity.
CoLoRA leverages task similarity to boost fine-tuning efficiency.
PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.
Develops an ordinal-similarity framework for scalable and interpretable representation alignment.
Study properties of self-similar continua with finite intersection property.
Proposes a method to evaluate meta-learning performance based on task similarity.
The article contains a construction of a self-similar dendryte which cannot be the attractor of any self-similar zipper.