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.
DCNs are fooled by Gabor noise patterns similar to adversarial perturbations.
Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing…
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity an…
GraphQ system uses GNNs to search for subgraph patterns in graphs.
Visuals in scientific papers are used to express complex ideas; this study uses them to identify knowledge domains.
Consensus dimension reduction combines multiple visualizations to identify shared patterns.
PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.
New VAE models reveal hierarchical visual cortex computations.
FDive helps analysts create relevant patterns in high-dimensional datasets.
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of obser…
TimeTrail detects financial fraud patterns through temporal correlation analysis.
FEALM learns features for better nonlinear DR of hidden patterns.
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
A new system detects and classifies defects in semiconductor manufacturing.
New visual tool detects financial market changes using multiscaling analysis.
We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can he…
SPREV simplifies visualization of complex labeled datasets.
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with simila…
Interactive tool for better understanding t-SNE projections.
A new VAD method uses respiration patterns from video to detect speech.
Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of 'domain knowledge'. We investiga…
Visual design improves financial data classification accuracy.
Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she…
We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over million expenses made publicly available by the Brazilian government. This dataset has, up to now, remained widely untouched by machine learning methods. Our main contribut…
Unlike common cancers, such as those of the prostate and breast, tumor grading in rare cancers is difficult and largely undefined because of small sample sizes, the sheer volume of time needed to undertake on such a task, and the inherent difficulty of extracting human-observed patterns. One of the most challenging exa…
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras wit…
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an auto…
New method visualizes brain activity changes over time.
SigTime learns interpretable signatures from time series data.
Topological surgery in dimension is intrinsically connected with the classification of -manifolds and with patterns of natural phenomena. In this expository paper, we present two different approaches for understanding and visualizing the process of -dimensional surgery. In the first approach, we view the proc…
This paper improves MDS visualization by adjusting Wasserstein distances for heavy-tailed data.
The paper proposes a method to identify high-quality financial patterns using entropy.
This paper compares imputation and direct parameter estimation methods for missing data in correlation matrix visualization.
Regshock visualizes financial risks to help regulators manage systemic shocks.
A new model analyzes document structure and customer shopping patterns.
Enhances group convolutional networks with attention to learn meaningful relationships.
DHRL learns interpretable features from visual data.
A novel supervised visualization technique for data exploration.
Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and…
Improved few-shot visual reasoning with image preprocessing.
Graphs model human mobility patterns, reducing errors in data matching.
A new model of V1 using orientation, frequency, and phase.
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algo…
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their…
New method evaluates text-to-image synthesis for realism, variety, and semantic accuracy.
Study detects anomalies in robot vision data to predict hazards.
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitr…
JD.com uses a new CNN model to improve ad click prediction.