A faster method for visualization recommendations on large datasets.
problem Infeasibility of state-of-the-art vis-rec models on large datasets due to high computational time.
method Reinforcement-learning (RL) framework that identifies optimal statistics within a time budget.
result Significantly reduces time-to-visualize with minimal error compared to baseline approaches.
This work aims to create a large-scale model for critical care time series data.
problem Lack of large-scale datasets and distribution shifts in critical care time series data.
method Harmonized dataset creation and transfer learning research.
result Established a foundation for large-scale multi-variate time series models in critical care.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
This paper compresses large datasets for efficient machine learning.
problem Efficiently processing large datasets for machine learning.
method Constructing a sketch of the dataset using random features and averaging, then learning from the sketch.
result The approach can perform machine learning tasks without full dataset access, preserving both information and privacy.
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via co…
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anoma…
Paper introduces scalable clustering for large datasets with outliers.
problem Lack of scalable algorithms for large datasets with outliers.
method Provable robust clustering algorithm based on loss minimization for Gaussian mixture models.
result Algorithm provides high accuracy with theoretical guarantees and outperforms existing methods.
This paper improves credit scoring models using a novel dataset distillation technique.
problem Limited scalability of pretrained models for tabular credit scoring datasets.
method Integrates class imbalance-aware dataset distillation with pretrained models.
result Improved AUC by 2.5% in financial datasets.
The paper proposes an efficient method to scale Bayesian inference for mixed multinomial logit models to very large datasets.
problem Efficiency in Bayesian inference for mixed multinomial logit models on large datasets.
method Amortized Variational Inference with stochastic backpropagation, automatic differentiation, and GPU acceleration.
result The proposed method achieves significant computational speedups over traditional methods for large datasets.
MIC consistently estimates dependence in large datasets.
problem Estimating dependence between variable pairs in large datasets.
method Proving consistency of MIC as an estimator.
result MIC is a consistent estimator of population statistic MIC*.
New method clusters large datasets using geometric properties.
problem Clustering large datasets using DBSCAN* and HDBSCAN* is infeasible.
method Exploiting Euclidean space geometry, systematically construct clusters from subsets of data.
result Clusters of large datasets are possible with controlled subset sizes.
FSL-Net detects and localizes feature shifts in large, high-dimensional datasets.
problem Feature shifts between data sources lead to erroneous features in various applications.
method FSL-Net is a neural network trained on multiple datasets to localize feature shifts.
result FSL-Net accurately localizes feature shifts from unseen datasets without re-training.
The paper investigates ethical issues in large image datasets, focusing on pornographic content.
problem Ethical issues in large-scale computer vision datasets, particularly concerning pornographic content.
method Cross-sectional model-based quantitative census covering various factors in the ImageNet-ILSVRC-2012 dataset.
result The dataset contains verifiably pornographic images, including non-consensual and voyeuristic content.
New method speeds up Gaussian process training and inference for large datasets.
problem Training and inference in Gaussian processes are computationally expensive for large datasets.
method Iterative alternating projection method that accesses subblocks of the kernel matrix, reducing time and space complexity.
result Empirically, the method accelerates GP training and inference by up to 72x compared to conjugate gradients.
Approaches for approximating persistent homology for large datasets.
problem Inability to compute persistent homology for large datasets.
method Multiple subsampling framework for statistical approximation of persistent homology.
result Derivation of finite sample convergence rates for empirical means of persistent homology.
New kernel model scales to large datasets.
problem Challenges in scaling kernel machines to large datasets.
method Introduced EigenPro 3.0, an algorithm based on projected dual preconditioned SGD.
result Ability to scale model and data sizes independently.
This paper tackles scalability issues in kernel logistic regression for large datasets.
problem Challenges in training large-scale kernel-based models for discrete choice modelling.
method Introduces Nyström approximation for Kernel Logistic Regression (KLR) on large datasets.
result The k-means Nyström KLR approach is a successful solution for large datasets, maintaining robust performance.
For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approxim…
Quantum machine learning tackles large datasets with randomized measurements.
problem Efficiently process large, high-dimensional datasets on quantum computers.
method Randomized measurements to scale linearly with dataset size and quadratic for post-processing.
result Substantial speed-up for noisy quantum computers, enabling image classification.
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the datase…
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…
Survey on using large models to train smaller datasets in NLP.
problem Lack of large datasets and computing resources for NLP tasks.
method Analysis of recent transfer learning approaches in NLP.
result Increased demand for transfer learning in NLP due to large models.
New scalable methods for robust model learning from large datasets.
problem Training robust models resistant to data distribution shifts.
method Composite optimization for distributionally robust optimization (DRO).
result Scalable methods for learning robust models from large datasets.
Theoretical analysis of data quality and synergies in LLMs.
problem Understanding why different training methods require different amounts of data.
method Theoretical analysis of transformers trained on a weight prediction task for linear regression.
result SFT excels on smaller datasets challenging for the pretrained model, while RL benefits from large, not overly difficult data.
New algorithm selects variables from large datasets.
problem Automatic selection of variables from large datasets.
method Uses Graphical Models and combines with OLS method.
result Outperforms LASSO method in forecasting models.
A hierarchical approach improves classification accuracy in large datasets.
problem Improving classification accuracy in large datasets with high dimensionality.
method Hierarchical subspace learning to scale manifold learning methods.
result Average 5% increase in classification accuracy.
PASHA optimizes model tuning for large datasets with limited resources.
problem Expensive HPO and NAS for large datasets.
method Dynamic resource allocation approach.
result Significantly reduces computational resources while maintaining performance.
A new large-scale tabular benchmark for Learning from Label Proportions.
problem Lack of a large-scale open benchmark for tabular Learning from Label Proportions.
method Proposed LLP-Bench, a suite of 70 datasets (62 feature bag and 8 random bag) from real-world tabular data.
result Demonstrated the effectiveness of 9 SOTA and popular tabular LLP techniques on 62 feature bag datasets.
Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of u…
The current study proposes a dimension reduction method, stepwise support vector machine (SVM), to reduce the dimensions of large p small n datasets. The proposed method is compared with other dimension reduction methods, namely, the Pearson product difference correlation coefficient (PCCs), recursive feature eliminati…
ParK efficiently solves kernel ridge regression for large datasets.
problem Large-scale kernel ridge regression efficiency and accuracy.
method Partitioning feature space with random projections and iterative optimization.
result Provably maintains statistical accuracy with reduced space and time complexity.
This work improves Gaussian process model selection for large datasets.
problem Prohibitively high computational cost in Gaussian process model selection.
method Linear-time scaling and computational uncertainty tradeoff.
result Computation-aware Gaussian processes can be trained on large datasets efficiently.
Paper introduces a new method to compute pseudoinverse for ELM with large datasets.
problem Efficient computation of pseudoinverse for ELM with large datasets.
method Rank-based matrix decomposition of the hidden layer matrix.
result Optimal training time and reduced computational complexity for large hidden nodes.
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …
ABMLL adapts LLMs to multiple datasets efficiently.
problem Limited generalization across multiple datasets in LLMs.
method Amortized Bayesian Meta-Learning for LoRA (ABMLL).
result ABMLL supports effective generalization across datasets and scales to large models.
NIST CTS Superset offers a large dataset for telephony speaker recognition.
problem Lack of a large-scale, uniform dataset for telephony speaker recognition.
method Compilation of speech segments from multiple corpora, including Greybeard, Switchboard, and Mixer series.
result Results on the NIST 2020 CTS Speaker Recognition Challenge serve as a reference baseline.
ADASAP accelerates GP inference for large datasets.
problem Scaling Gaussian process inference to large datasets.
method Approximate sketch-and-project algorithm for solving linear systems.
result Sketch-and-project rapidly converges to true posterior mean.
Large speech dataset for commercial use with 9.98% word error rate.
problem Creating a diverse speech recognition dataset for commercial purposes.
method Internet search for licensed audio data with transcriptions, training model on the dataset.
result Model trained on dataset achieves 9.98% word error rate on Librispeech's test-clean test set.
Sketching algorithm finds closest point on convex hull efficiently.
problem Finding the closest point on a convex hull of large datasets.
method Sketching procedure to exploit data structure, gradient project method.
result Faster solution than standard optimization algorithms.
A new algorithm improves efficiency in selecting examples for deep learning.
problem Efficiently choosing multiple examples to mark up for deep learning on large datasets.
method Large BatchBALD algorithm, approximating BatchBALD with reduced computational complexity.
result Comparable quality in selection while significantly reducing computation time, especially for large batches.
AnomalyCD discovers anomaly causes in large systems with binary flags, reducing computational burden.
problem Learning graphical causal models from large-scale binary anomaly data is computationally expensive.
method AnomalyCD uses anomaly data-aware causality testing, sparse data compression, and edge pruning.
result AnomalyCD reduces computation overhead and improves accuracy on binary anomaly datasets.
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance de…
TSFMs improve financial forecasting from diverse datasets.
problem Challenges in forecasting financial time series due to noisy, non-stationary, and heterogeneous data.
method Empirical study of TSFMs in global financial markets, evaluating zero-shot inference, fine-tuning, and pre-training from scratch.
result Pre-trained TSFMs on financial data achieve substantial forecasting and economic improvements, highlighting the value of domain-specific adaptation.
Paper proposes data quality measures for large-scale high-dimensional data.
problem Lack of practical data quality measures for large-scale high-dimensional data.
method Proposes two data quality measures: class separability and in-class variability. Efficient algorithms based on random projections and bootstrapping are provided.
result Efficient algorithms for computing data quality measures on large-scale high-dimensional data.
Theory explains neural network scaling with dataset and model size.
problem Neural network scaling laws with dataset and model size.
method Identified variance-limited and resolution-limited scaling behaviors.
result Four scaling regimes explained: infinite data, infinite width, resolution-limited, and large width.
This paper benchmarks FinGPT for financial datasets using open-source large language models.
problem Challenges in integrating GPT-based models with financial datasets.
method Instruction Tuning paradigm for open-source large language models adapted for financial contexts.
result Demonstrates the effectiveness and adaptability of FinGPT in financial tasks.
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results. The projection-based…
Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of …