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Found 29 papers, 5 papers with code
Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach
Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs.
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Recently, deep learning has facilitated the analysis of high-dimensional single-cell data.
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data.
Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion
We also provide a novel strategy for determining the kernel width which ensures that our method can efficiently exploit information redundancy supplied by relative motions in the presence of many outliers.
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks.
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization
Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement.
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems.
Statistical patterns of word frequency suggesting the probabilistic nature of human languages
Traditional linguistic theories have largely regard language as a formal system composed of rigid rules.
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability.
Deep Latent-Variable Kernel Learning
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model.
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.
Anomaly Detection via Graphical Lasso
Accordingly, we propose a novel optimization problem that is similar in spirit to Robust Principal Component Analysis (RPCA) and splits the sample covariance matrix $M$ into two parts, $M=F+S$, where $F$ is the cleaned sample covariance whose inverse is sparse and computable by Graphical Lasso, and $S$ contains the outliers in $M$.
Large-scale Heteroscedastic Regression via Gaussian Process
To improve the scalability, we first develop a variational sparse inference algorithm, named VSHGP, to handle large-scale datasets.
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness.
Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation
A computer simulation based on the dual-process theory yields Zipf's law with the same structural pattern, suggesting that Zipf's law of natural languages are motivated by common cognitive mechanisms.
When Gaussian Process Meets Big Data: A Review of Scalable GPs
The review of scalable GPs in the GP community is timely and important due to the explosion of data size.
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.
Existence of Hierarchies and Human's Pursuit of Top Hierarchy Lead to Power Law
The power law is ubiquitous in natural and social phenomena, and is considered as a universal relationship between the frequency and its rank for diverse social systems.
The distribution of information content in English sentences
Sentence is a basic linguistic unit, however, little is known about how information content is distributed across different positions of a sentence.
Dependency length minimization: Puzzles and Promises
In the recent issue of PNAS, Futrell et al. claims that their study of 37 languages gives the first large scale cross-language evidence for Dependency Length Minimization, which is an overstatement that ignores similar previous researches.
The influence of Chunking on Dependency Crossing and Distance
These results suggest that chunking may play a vital role in the minimization of dependency distance, and a somewhat contributing role in the rarity of dependency crossing.
The risks of mixing dependency lengths from sequences of different length
However, the empirical distribution of dependency lengths of sentences of the same length differs from that of sentences of varying length and the distribution of dependency lengths depends on sentence length for real sentences and also under the null hypothesis that dependencies connect vertices located in random positions of the sequence.
