Researchers introduce datasets for cursive Japanese to ML community.
problem Engage ML community with classical Japanese literature datasets.
method Developed three datasets: Kuzushiji-MNIST, Kuzushiji-49, and Kuzushiji-Kanji.
result Introduced datasets focusing on cursive Japanese to ML community.
Script Geometry offers a new approach to discrete differential geometry.
problem Classic approaches to discrete differential geometry have limitations.
method Script Geometry uses complexes of cells and minimality conditions called tightness.
result Script Geometry provides an equivalence to the Poincare lemma and new function theory.
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can …
Vamsa tracks data usage in Python scripts for ML models.
problem Automatically tracking data used in ML model training.
method Modular system that extracts provenance from Python scripts.
result Vamsa achieves high precision and recall in tracking data usage.
We describe a collection of computer scripts written in PARI/GP to compute, for reflection groups determined by finite-volume polyhedra in H3, the commensurability invariants known as the invariant trace field and invariant quaternion algebra. Our scripts also allow one to determine arithmeticity of such gr…
In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script. We present an end-to-end framework that segments the text image, classifies the characters and extracts lines using a language model. The segmentation is based on mathematical morphology. The classification module, which is …
Python scripts analyze MRAM-based neuromorphic devices' process variation impacts on machine learning accuracy.
problem Impact of process variation on MRAM-based neuromorphic devices' performance in machine learning applications.
method Developed transportable Python scripts to analyze output variation under changes in device dimensions.
result Revealed impacts and limits for processing variation of device fabrication on energy vs. accuracy tradeoffs.
A movie multilayer network model captures narration from script, subtitles, and content.
problem Discovering content and stories in movies using network models.
method Developed a multilayer network model using visual and textual semantic cues.
result Demonstrated the effectiveness of the model on the Star Wars saga.
New handwritten digits dataset for Kannada script.
problem Lack of datasets for Kannada numeral digits.
method Developed Kannada-MNIST and Dig-MNIST datasets.
result Initial CNN accuracy is lower than MNIST, indicating a challenge in generalization.
KaoKore dataset extracts faces from pre-modern Japanese art for machine learning.
problem Lack of relevant datasets for historical Japanese artworks in machine learning.
method Extracted faces from pre-modern Japanese artwork to create a new dataset.
result Demonstrated the dataset's value for image classification and creative applications.
For the numerical solution of the American option valuation problem, we provide a script written in MATLAB implementing an explicit finite difference scheme. Our main contribute is the definition of a posteriori error estimator for the American options pricing which is based on Richardson's extrapolation theory. This e…
Formula derived for Laplace-Beltrami spectrum on homogeneous spaces.
problem Calculating the spectrum of the Laplace-Beltrami operator on homogeneous spaces.
method Formula derivation based on eigenvalues of a generalized Casimir operator and spherical representations.
result First detailed computation and investigation of the spectrum for a family of metrics on the Aloff-Wallach manifold.
New method explains high-dimensional text classifiers.
problem Limited explainability tools for high-dimensional inputs and neural networks.
method Theoretical high-dimensional properties in neural networks.
result Improved explainability for neural network classifiers.
Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations o…
We model anomaly and change in data by embedding the data in an ultrametric space. Taking our initial data as cross-tabulation counts (or other input data formats), Correspondence Analysis allows us to endow the information space with a Euclidean metric. We then model anomaly or change by an induced ultrametric. The in…
ChainerRL is a deep reinforcement learning library for Python.
problem Training and reproducing deep reinforcement learning algorithms.
method Built on Chainer framework, implements various DRL algorithms.
result Fosters reproducible research and replicates benchmark results.
GluonCV and GluonNLP simplify deep learning for CV and NLP.
problem Lack of easy-to-use deep learning tools for CV and NLP.
method Developed modular APIs and pre-trained models for rapid prototyping.
result Facilitates rapid prototyping and reproducible research in CV and NLP.
Dataset analyzes tweets' impact on stock returns.
problem Understanding how public opinion affects stock market outcomes.
method Created a dataset of 862,231 labeled tweets, provided baselines and multi-view learning approach.
result Demonstrated the impact of tweets on stock returns over various time frames.
Hybrid approach combines ASTs and deep learning for PowerShell malware detection.
problem Detecting malicious PowerShell scripts effectively.
method Combining static program analysis (ASTs) with deep learning.
result Preliminary results show promising classification of PowerShell scripts by family type.
We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dála, members of parliament), such…
This handbook translates lead time analysis into R code.
problem Tracking divergence in booking lead times over time.
method Translated original article's methodology into R code.
result Demonstrated reproducibility and error bounds in lead time forecasts.
Tune simplifies hyperparameter search for distributed computing.
problem Adapting hyperparameter search to distributed environments.
method Unified framework for model selection and training.
result Simplifies implementation of state-of-the-art search algorithms.
In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1…
A new RNN architecture 'deductron' for complex character sequences.
problem Complex character sequences requiring long-term memory.
method Constructed a simple writing system and guessed RNN weights.
result Demonstrated that a 3-layer deductron can be trained.
The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequen…
ConEx learns complex embeddings for knowledge graphs, improving link prediction.
problem Predicting missing links in knowledge graphs.
method 2D convolution with Hermitian inner product of complex-valued embeddings.
result ConEx outperforms state-of-the-art methods on various benchmarks.
This paper compares analytical and numerical solutions of the Black-Scholes model.
problem Comparing analytical and numerical methods for solving the Black-Scholes model.
method Analytical solution (variable separation) and numerical solution (finite differences) of the Black-Scholes equation.
result Numerical solutions provide more accurate results for complex scenarios.
The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates th…
Regulating crypto and DeFi for inclusive economic advancement.
problem Innovative financial systems pose challenges to traditional regulatory frameworks.
method Formulating regulatory structures that balance innovation and consumer protection.
result Regulatory frameworks are essential for leveraging crypto and DeFi for inclusive economic growth.
New stable HOIF estimators for statistical functionals.
problem Constructing numerically stable HOIF estimators for statistical functionals.
method Developed new sHOIF estimators with provable guarantees.
result 2nd order sHOIF estimators were validated in synthetic experiments.
Research designs first model for Amharic handwritten character recognition.
problem No existing model for Amharic handwritten character recognition.
method Used a convolutional neural network and applied data augmentation and multi-task learning.
result Promising results observed from the enhanced model.
ORIL learns a reward function from unlabeled data to improve robot learning.
problem Leveraging unlabeled data for robot learning.
method ORIL learns a reward function from demonstrator and unlabeled trajectories, annotates data, and trains an agent via offline reinforcement learning.
result ORIL consistently outperforms BC agents on various robotic tasks.
Paper presents datasets from European Court of Human Rights judgments for classification studies.
problem Lack of accessible and reproducible datasets for legal judgments.
method Automated open-source scripts for data collection and feature transformation; experimental campaign on machine learning algorithms.
result Consistently good accuracy (75.86% - 98.32%) across binary datasets, with an average accuracy of 96.45%.
PDSim simulates and estimates commodity futures prices using polynomial diffusion models.
problem Simulating and estimating commodity futures prices using polynomial diffusion models.
method Developed an R package with a Shiny app for simulation and estimation of commodity futures prices using polynomial diffusion models.
result PDSim is the only package specifically designed for the simulation and estimation of the polynomial diffusion model.
We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNet, the toolkit offers scalable training a…
CAT toolkit combines hybrid and E2E approaches for efficient speech recognition.
problem Improving speech recognition efficiency and latency.
method CTC-CRF based framework with contextualized soft forgetting.
result CAT achieves state-of-the-art results with simpler training and streaming ASR.
Survey of deep learning for Hindi text classification.
problem Limited research on morphologically rich, low-resource Hindi text classification.
method Comparison of CNN, LSTM, Transformer, BERT, and LASER for Hindi text classification.
result Multilingual pre-trained sentence embeddings outperform traditional architectures for Hindi text classification.
Triangulation filters spurious circuits in multilingual models.
problem Unreliable explanations of multilingual models across languages.
method Formalizes reference families and introduces triangulation as a causal acceptance rule.
result Triangulation provides a falsifiable standard for mechanistic claims.
CPR adds entropy maximization to improve continual learning methods.
problem Catastrophic forgetting in continual learning.
method Classifier-Projection Regularization (CPR) adds an entropy maximization term to existing regularization methods.
result CPR improves accuracy and plasticity in continual learning methods.
Open dataset for machine learning with reduced high-angle artefacts.
problem High-angle artefacts in cone-beam CT data for machine learning.
method Open data collection of 42 walnuts with varied cone angles, combined for artefact reduction.
result Ground truth images from combined data for supervised learning.
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
This paper introduces an acceleration structure for hyperbolic embeddings.
problem Efficiently embedding and visualizing high-dimensional data in hyperbolic spaces.
method Building upon a polar quadtree, the paper introduces a new acceleration structure for hyperbolic embeddings.
result The new method computes embeddings in significantly less time compared to existing methods.
LLMs help automate extraction of actuarial variables from unstructured claims data.
problem Manual processing of unstructured claims data is time-consuming and inconsistent.
method Two-stage processing architecture using LLMs, modular Python pipeline.
result LLM-based extraction achieved high accuracy and practical actuarial value.
Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
problem Fairness issues in criminal justice risk assessment algorithms.
method Adopting conformal prediction sets to remove unfairness from algorithms and covariates.
result Constructs confusion tables and measures fairness effectively free of racial differences.
Study evaluates ML methods for two-sample testing with right-censored data.
problem Evaluating ML methods for two-sample testing with right-censored data.
method Developed and compared several ML-based methods with classical tests.
result Proposed methods outperform classical tests in terms of statistical power.
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for …
M-PHATE visualizes neural network learning dynamics.
problem Understanding neural network performance and learning dynamics.
method Multislice PHATE (M-PHATE) for visualizing neural network hidden representations.
result M-PHATE provides detailed summaries of learning dynamics without needing validation data.
New accuracy measure Ha improves AI system assessment in clinical practice.
problem Inadequate metrics for assessing AI system performance in clinical settings.
method Introducing H-accuracy (Ha) as a more informative measure.
result H-accuracy is a generalization of balanced accuracy and related to Net Benefit.