New neuromorphic hardware learns MNIST digits efficiently.
problem Limited scalability and in-hardware learning in existing neuromorphic hardware.
method Low-cost scalable NoC-based SNN architecture with in-hardware STDP learning.
result Demonstrated learning capability of the hardware architecture.
A new 3D NoC design optimizes heterogeneous manycore systems for deep learning.
problem Designing energy-efficient 3D manycore platforms for deep learning.
method ML-based multi-objective optimization for heterogeneous requirements.
result 9.6% better Energy-Delay Product compared to thermally-optimized designs.
Scalable spaces are simply connected manifolds with nice cohomology properties.
problem Understanding the limitations of formality in higher homotopy groups.
method Analyzing the embedding of cohomology algebras into differential forms.
result Spaces that are formal but not scalable provide counterexamples to Gromov's conjecture.
This paper investigates scalable Gaussian process regression methods for big data.
problem Scalability issues in Gaussian process regression for big data.
method Investigates various scalable Gaussian process methods including sparse approximations and local aggregations.
result Most scalable GPs have linear scalability to training size, capturing different spatial and local patterns.
Bayesian optimization sped up with scalable Gaussian processes.
problem Optimizing functions with derivative information and large datasets.
method Combines derivative acceleration and scalable Gaussian process models.
result Significant speedup in optimization convergence for large datasets.
New scalable algorithm improves linear regression accuracy and efficiency.
problem Improving linear regression accuracy and efficiency for large datasets.
method Developed new theory to model significance and multicollinearity as constraints, scaling with n in the 10,000s. result Significantly improves accuracy, reduces false detection rate, and speeds up computation.
The paper discusses scalable learning for wireless data-driven systems.
problem Expanding data volume and model complexity limit centralized learning solutions.
method Discusses scalable architecture and local learning strategies.
result Promising research directions in scalable data-driven wireless communications.
Unified scalable GPCs for various likelihoods using additive noise.
problem Scalability issues and intractable inference in GPC for big data and non-Gaussian likelihoods.
method Additive noise to unify scalable GPCs for multiple likelihoods, using variational inference.
result Empirically superior results for binary/multi-class classification tasks with up to two million data points.
Scalable3-BO tackles scalability issues in Bayesian optimization for big data and high dimensions.
problem Bayesian optimization scalability issues in big data and high dimensions.
method Sparse Gaussian process, random embedding, asynchronous parallelization.
result Scalable3-BO framework optimizes high-dimensional problems with 1 million data points and 10,000 dimensions.
GRAM generates scalable graphs with a novel attention mechanism.
problem Scalability in graph generation for large datasets.
method GRAM uses a graph attention mechanism to generate scalable graphs.
result GRAM outperforms baseline methods in scalability and quality.
New scalable Lipschitz bounds improve neural network robustness analysis.
problem Computing tight Lipschitz bounds for deep neural networks is challenging and computationally expensive.
method Derived new closed-form Lipschitz bounds using more general feasible points of LipSDP, avoiding SDP solvers.
result Improved scalability and precision of Lipschitz estimation for large neural networks.
SAFE automates feature engineering for industrial tasks efficiently and scalably.
problem Efficiency and scalability of automatic feature engineering methods for industrial tasks.
method SAFE (Scalable Automatic Feature Engineering) method, which provides excellent efficiency and scalability.
result SAFE method provides prominent efficiency and competitive effectiveness in industrial tasks.
New GP-VAE model improves scalability and performance.
problem Inability of conventional VAEs to model correlations between data points.
method Principled sparse inference approaches to improve scalability of GP-VAEs.
result New model outperforms existing approaches in runtime and memory usage.
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
problem Detecting changes in data streams efficiently and reliably.
method Bayesian online changepoint detection with provable robustness and scalability.
result The proposed method is more than 10 times faster than previous approaches and provides provable robustness.
SIGL learns scalable graphons from graphs.
problem Estimating graphons from graphs of varying sizes.
method Combines INRs and GNNs for scalable graphon estimation.
result SIGL learns consistent graphons at arbitrary resolutions.
Scalable TS using sparse GPs improves efficiency without sacrificing performance.
problem Efficiently applying TS to complex, multi-modal problems.
method Sparse Gaussian Process models for scalable TS.
result Theoretical and empirical validation of scalable TS's effectiveness.
A scalable parallel BO method for asynchronous settings.
problem Expensive-to-evaluate problems in machine learning.
method Simple and scalable Bayesian optimization method for asynchronous parallel settings.
result Demonstrated promising performance on benchmark functions and hyperparameter optimization.
Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
problem Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
method Reformulating Stein discrepancy construction as an explicit SNR^2 maximisation problem
result Avoiding exponential SNR^2 collapse and achieving stable SNR^2
The article uses PageRank and persistent homology for scalable graph comparison.
problem Comparing the similarities between complex networks.
method Combines PageRank and persistent homology to compute a scalable graph descriptor.
result Shows the effectiveness of the method on shape mesh datasets.
PyOD offers scalable outlier detection for multivariate data.
problem Scalable outlier detection for multivariate data.
method Wide range of outlier detection algorithms, including ensembles and neural networks.
result Robust and scalable outlier detection.
New scalable GP approximation using Fourier series decomposition.
problem Scalability and accuracy in Gaussian process approximations.
method Harmonic kernel decomposition (HKD) to decompose kernels orthogonally.
result Significantly outperforms standard variational methods in scalability and accuracy.
Scalable GAMs using sparse variational Gaussian processes.
problem Flexible modeling of data beyond linear models.
method Bayesian treatment of GAMs using Gaussian processes (GPs) with sparse representation and additive structure.
result Efficient and well-calibrated Bayesian treatment of GAMs.
dAUTOMAP scales AUTOMAP by decomposing domain transformation.
problem Inadequate scalability limits AUTOMAP's practicality.
method Decomposes AUTOMAP's domain transformation for linear scalability.
result dAUTOMAP outperforms AUTOMAP with fewer parameters.
A scalable algorithm for GP regression selects relevant covariates efficiently.
problem Scalable variable selection in large GP regression models.
method VGPR algorithm using Vecchia approximation for sparse precision matrix, mini-batch subsampling.
result Improved scalability and accuracy in selecting relevant covariates.
Developing stable and scalable probabilistic ODE solvers for stiff and high-dimensional problems.
problem Stiff and high-dimensional ODEs
method Matrix-free update step and iterative re-linearization
result Improved stability and scalability
The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP) regression, a well-known non-parametric and interpretable Bayesian model, which suffers from cu…
Scalable model for slate recommendation learns reward probabilities.
problem Scalable personalized slate recommendation in large action spaces.
method Probabilistic Rank and Reward (PRR) model combining reward, interaction, and rank.
result PRR outperforms existing methods and is scalable to large action spaces.
This paper develops a scalable Thompson Sampling method using optimal transport.
problem Efficiently approximating posterior distributions for complex models in Thompson Sampling.
method The approach uses distribution optimization techniques via Wasserstein gradient flows to approximate posterior distributions efficiently.
result The proposed method achieves superior performance on both synthetic and real large-scale data.
New framework scales graph AE and VAE by training on a subset of nodes.
problem Training scalability and speed issues in graph AE and VAE models.
method Utilizes graph degeneracy to train on a dense subset of nodes, with a propagation mechanism.
result Empirically competitive results on large graphs (millions of nodes and edges).
TensorHyper-VQC improves VQC scalability and robustness.
problem Scalability and noise sensitivity in VQC.
method Tensor-train-guided hypernetwork framework.
result TensorHyper-VQC achieves superior performance and robust noise tolerance.
Paper combines scalable BMF algorithms for web-scale datasets.
problem High computational cost of Bayesian Matrix Factorization.
method Combines Posterior Propagation and asynchronous distributed implementation.
result Substantial improvements in scalability on web-scale datasets.
AIDEL improves scalability of learned indexes in storage systems.
problem Expensive retraining and heavy inter-model dependency in learned indexes limit scalability.
method Construct different linear regression models based on data distribution, making them independent and easier to partition.
result AIDEL improves insertion performance by about 2x and comparable lookup performance.
FISHDBC clusters arbitrary data with flexible, scalable, and hierarchical features.
problem Clustering arbitrary data with arbitrary distance functions efficiently.
method Flexible, incremental, scalable, hierarchical density-based clustering algorithm.
result Flexible clustering of arbitrary data without feature extraction.
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point a…
Two novel algorithms improve scalability and robustness of spectral clustering for large datasets.
problem Scalability and robustness of spectral clustering for large-scale datasets.
method Ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC) algorithms.
result Robust and efficient clustering of ten-million-level datasets on a PC.
S3VDC improves DC methods for scalability, stability, and simplicity.
problem Poor scalability, instability, and lack of simplicity in DC methods.
method Four algorithmic improvements: initial γ-training, periodic β-annealing, mini-batch GMM initialization, and inverse min-max transform. S3VDC incorporates all improvements. result S3VDC outperforms state-of-the-art methods on benchmark and industrial datasets.
LIBS2ML is a fast, scalable library for second order machine learning.
problem Large-scale machine learning problems in big data.
method Scalable second order learning algorithms using MEX files.
result Efficient solution for large-scale learning problems.
A scalable MARL algorithm using local rewards for cooperative multi-agent learning.
problem Scalability issues in cooperative multi-agent reinforcement learning due to large state and action spaces.
method LOMAQ algorithm incorporating local rewards in centralized training and decentralized execution.
result LOMAQ scales well compared to other methods, improving performance and convergence speed.
Scalable PnP-ADMM for large-scale imaging problems.
problem Heavy computational and memory requirements of current PnP algorithms.
method Incremental variant of PnP-ADMM with theoretical convergence guarantees.
result Fast convergence and scalability compared to existing PnP algorithms.
BNAS improves neural architecture search with a scalable, fast, and efficient approach.
problem Efficiently searching for optimal neural architectures with high performance and low training time.
method Designing a broad scalable architecture (BCNN) with reinforcement learning and parameter sharing, and developing two variants.
result Significantly reduces training time and achieves state-of-the-art performance on CIFAR-10 and ImageNet.
New method for scalable barycenter computation using Wasserstein gradient flows.
problem Scalability and integration of label information in barycenter computation.
method Gradient flows in Wasserstein space, time discretization, mini-batch optimal transport, modular regularization, task-aware functions, supervised information integration.
result Empirically validated new state-of-the-art barycenter solver with labeled barycenters outperforming unlabeled ones.
This paper improves parallel belief propagation for scalable machine learning.
problem Efficient parallelization of belief propagation for large-scale machine learning tasks.
method Use of scalable relaxed schedulers to parallelize belief propagation.
result Our approach outperforms previous methods in scalability and convergence time.
Paper proposes scalable algorithm to estimate intervention targets in linear models.
problem Estimating intervention targets in linear models from observational and interventional data.
method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.
result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.
The book covers scalable MCMC methods for Bayesian learning.
problem Scalability issues in Bayesian learning with large datasets.
method Advanced MCMC algorithms, including stochastic gradient, non-reversible, and continuous time methods.
result Substantial advances in practical and theoretical Bayesian computation.
This study improves scalability of randomized smoothing for certifying classifier robustness.
problem Certifying machine learning classifiers against adversarial attacks is challenging and scalable solutions are needed.
method The study reviews and explores randomized smoothing and its derivatives, focusing on scalability.
result The study provides theoretical guarantees and discusses scalability challenges of randomized smoothing.
This paper surveys scalable automated alignment methods for LLMs.
problem Scalability issues in traditional human-annotated alignment methods for LLMs.
method Categorizes and discusses various automated alignment methods.
result Emerging automated alignment methods are effective and scalable.
New method improves scalability of Gaussian processes for discrete data.
problem Scalability issue in Gaussian processes for discrete domains.
method Simulated annealing for selecting inducing points.
result Simulated annealing outperforms SVM and full GP on DNA sequence data.
DKL-KAN combines deep learning and kernel methods for scalable, expressive models.
problem Combining deep learning's depth with kernel methods' flexibility for scalable models.
method DKL-KAN uses Kolmogorov-Arnold Networks (KAN) to optimize kernel attributes within a Gaussian process framework.
result DKL-KAN outperforms DKL-MLP on datasets with a low number of observations and DKL-MLP on large datasets.