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.
Paper introduces hierarchical softmax for global hierarchical classification tasks.
Boxhead dataset tests autoencoder disentanglement in hierarchical data.
Neural NMF discovers hierarchical topics in multilayer data.
Combines geometry and topology for analyzing hierarchical datasets.
Typically, Softmax is used in the final layer of a neural network to get a probability distribution for output classes. But the main problem with Softmax is that it is computationally expensive for large scale data sets with large number of possible outputs. To approximate class probability efficiently on such large sc…
A new distance for mixed-variable, hierarchical datasets with meta variables.
New hierarchical model improves on standard practice for high-dimensional data.
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…
This paper speeds up inference in large hierarchical models.
Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure b…
A clustering algorithm for natural hierarchical clusters with near-linear time complexity.
Paper proposes a Renyi entropy-based method for tuning hierarchical topic models.
A hierarchical approach improves classification accuracy in large datasets.
We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network--a composit…
Sparse model for noisy datasets using hierarchical regularization.
Researchers develop methods for inference in hierarchical models using neural simulations.
New algorithm B++&C improves hierarchical clustering on large deep embedding datasets.
Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.
Hierarchical-CPI improves variable importance measurement for medical data.
Improved deep hierarchical VAE with diffusion-based VampPrior.
Diffusion models reveal latent hierarchical structure in data.
We learn hierarchical slate representations for collaborative filtering.
Proposes a hierarchical clustering method for positive and negative dissimilarities.
Visual objects are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance. However, object appearance is the compositional consequence of both an object's intrinsic and extrinsic c…
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), w…
Proposes HBGNN for better recommendation systems using graph neural networks.
Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarch…
This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the data items stored in the sub-tree beneath it, so that each level of the hierarchy corresponds to a pa…
Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not ta…
Wasserstein t-SNE embeds hierarchical datasets considering within-unit distributions.
Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have hierarchical relations, and classifiers exploiting …
End-to-end deep model for coherent probabilistic forecasts in hierarchical time series.
Enhances image classification by integrating semantic hierarchy into CNN models.
Recent progress on deep learning has made it possible to automatically transform the screenshot of Graphic User Interface (GUI) into code by using the encoder-decoder framework. While the commonly adopted image encoder (e.g., CNN network), might be capable of extracting image features to the desired level, interpreting…
A fair reward system boosts participation in federated learning.
Hierarchical clustering is a widely used approach for clustering datasets at multiple levels of granularity. Despite its popularity, existing algorithms such as hierarchical agglomerative clustering (HAC) are limited to the offline setting, and thus require the entire dataset to be available. This prohibits their use o…
A hierarchical clustering algorithm for data clouds without structure assumptions.
A novel unsupervised domain adaptation method using hierarchical optimal transport.
We consider a particular instance of a common problem in recommender systems: using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and sub-genres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across a…
Traditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem. Though there is a hierarchy in the way humans use language by forming paragraphs from sentences and sentences from words, hierarchical mod…
Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observations. Among them, a factorized hierarchical variational autoencoder (FHVAE) is a variational inference-based model that formulates a hiera…
A new method generates graphs with hierarchical structures.
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense linear algebra routi…
Reduces high granularity and dimensionality in hierarchical categorical variables.
Fair HAC algorithms ensure clustering fairness across protected groups.
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typ…
Hierarchical NMF organizes COVID-19 literature into a searchable tree.
This work improves metric learning models by incorporating class hierarchies.