This paper establishes for the first time the predictive performance of speed priors and their computational complexity. A speed prior is essentially a probability distribution that puts low probability on strings that are not efficiently computable. We propose a variant to the original speed prior (Schmidhuber, 2002),…
On-device research index
arXiv research
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.
169,181 papers · 148 categories
Trend · papers per month
48 results for “computation times”
Hybrid approach reduces computation time and decoding complexity.
problem Straggling servers in distributed computing.
method Coded partial gradient computation (CPGC) that balances gradient accuracy and completion time.
result Reduces both computation time and decoding complexity.
Investigates polynomial time algorithms for computing Khovanov homology of braids.
problem Computing Khovanov homology for general braids is intractable.
method Examines polynomial time algorithms for 3-braids and a variation of the scanning algorithm for more general braids.
result Shows that for 3-braids, Khovanov homology can be computed in polynomial time, while for more general braids, it can be computed in polynomial time for bounded homological degrees.
This paper provides a mathematical framework for time-delay reservoir computing.
problem Lack of rigorous mathematical foundations for reservoir computing properties.
method Control-theoretic framework, formal definitions of separation and fading memory, explicit lower bound derivation.
result Established formal definitions and connections to stability notions for time-delay systems.
Deep learning models' architectures, including depth and width, are key factors influencing models' performance, such as test accuracy and computation time. This paper solves two problems: given computation time budget, choose an architecture to maximize accuracy, and given accuracy requirement, choose an architecture …
MindFlayer SGD improves parallel SGD for heterogeneous, random compute times.
problem Minimizing nonconvex functions with heterogeneous, random compute times.
method MindFlayer SGD, designed for stochastic and heterogeneous delays.
result MindFlayer SGD outperforms existing methods in environments with heavy-tailed noise.
Computer vision SSL methods show effectiveness on time series data.
problem Evaluate if computer vision SSL frameworks are effective on time series data.
method Evaluated on UCR and UEA archives, proposed a new method improving VICReg.
result Computer vision SSL frameworks can be effective on time series data.
Adaptive neural network adjusts layer depth based on computational needs.
problem Deciding the number of layers in deep recurrent neural networks is challenging.
method Proposes a layer flexible recurrent neural network with adaptive computation time.
result Improves performance by 7\% to 12\% on financial and language data.
Method learns software resource usage from snapshots.
problem Challenges in learning time-varying, correlated resource usage.
method Graph structured Schrödinger bridge problem for nonparametric learning.
result Predicts most-likely resource distributions.
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learningcs.LG
ATA optimizes task allocation in distributed machine learning.
problem Greedy task allocation leads to inefficiencies in distributed machine learning.
method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.
result ATA identifies optimal task allocation without prior knowledge of computation times.
Adaptive PCA algorithm for real-time data analysis.
problem Real-time computation of eigenspace for time-varying data.
method Online adaptive PCA algorithm with one-step update rule considering second order correlations.
result The algorithm provides an excellent approximation to the original eigenspace computed using standard PCA in batch mode.
Adaptive compute allocation improves model performance by prioritizing harder queries.
problem Inefficiency in allocating test-time compute uniformly across all queries.
method Formulated as a bandit learning problem, proposed adaptive algorithms that estimate query difficulty and allocate compute accordingly.
result Achieved up to 15.29% relative performance improvement on various benchmarks.
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillationcs.CV
Real-time uncertainty estimation for computer vision tasks.
problem Real-time inference of uncertainty in deep learning models.
method Uncertainty-Aware Distribution Distillation method for fast inference.
result Significantly reduced inference time with improved uncertainty and predictive performance.
Paper optimizes distributed learning by reducing gradient computation time.
problem Efficiently compute gradients in distributed learning tasks.
method Recursive polynomial constructions for coding across data subsets and vector components.
result Optimal tradeoff between computation load, straggler tolerance, and communication cost achieved.
RNNs compute by warping neural representations over time.
problem Understanding how RNNs perform task computations.
method Developed a Riemannian geometric framework to derive the manifold topology and geometry of RNNs.
result Dynamic warping is a fundamental feature of RNN computations.
A new algorithm predicts periodic time series data efficiently in cloud environments.
problem Efficiently identifying and predicting periodic patterns in large-scale time-series data.
method Proposes a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm using TSDCA, MTSPPR, and PTSP methods.
result Significant improvements in prediction accuracy and performance compared to existing algorithms.
A hardware-based reservoir computing system predicts time series with high speed and accuracy.
problem Processing time-dependent signals with high speed and accuracy.
method A hardware-based reservoir computing system using a field-programmable gate array (FPGA) for both the reservoir and output layers.
result Achieves comparable accuracy to software approaches but with a superior real-time prediction rate up to 160 MHz.
Quantum Encoding and Analysis on Continuous Time Stochastic Process with Financial Applicationsquant-ph
Quantum computing speeds up analysis of financial stochastic processes.
problem Challenging simulation and analysis of continuous time stochastic processes.
method Established a quantum framework for efficient state preparation and information extraction.
result Extraction of path-dependent and history-sensitive information from stochastic processes efficiently.
Data-driven configuration tuning of glmnet for balancing accuracy and computational efficiencystat.CO
Optimizes glmnet configuration for better accuracy and efficiency.
problem Inappropriate glmnet configuration leads to inaccurate solutions and increased computation time.
method Data-driven framework using neural networks to predict accuracy and computation time from dataset characteristics and configuration.
result Automatic selection of optimal configuration maximizing accuracy under a time constraint.
Study computes isoperimetric profiles in low-dimensional Riemannian products.
problem Estimating isoperimetric profiles in Riemannian products.
method Symmetrization techniques for product manifolds.
result Explicit lower bounds for isoperimetric profiles in and .
QAlign improves language model alignment with less compute, outperforming existing methods.
problem Improving language model performance with limited test-time computation.
method QAlign: sampling from optimal aligned distribution using Markov chain Monte Carlo.
result Consistent improvements over existing methods on various benchmarks.
We describe a polynomial-time algorithm to compute a (tight) geodesic between two curves in the curve graph. As well as enabling us to compute the distance between a pair of curves, this has several applications to mapping classes. For example, we can use these geodesics to compute the asymptotic translation length, Ni…
Early stopping method saves up to 75% computation time in policy search tasks.
problem Lengthy evaluation times in optimization problems, especially in robotics.
method A generalized early stopping criterion that only uses objective value at each time step.
result The method saves up to 75% computation time compared to no stopping.
New algorithm for HOMFLY-PT polynomial reduces computation time.
problem Computing HOMFLY-PT polynomial is #P-hard.
method Fixed-parameter tractability in treewidth.
result HOMFLY-PT polynomial can be computed efficiently using sub-exponential time algorithm.
Anytime Monte Carlostat.CO
Monte Carlo algorithms simulate some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of samples simulated being random. To complicate matters, the real time t…
Two new algorithms speed up TreeSHAP computation for tree-based models.
problem Slow computation of SHAP values on tree-based models.
method Two new algorithms, Fast TreeSHAP v1 and v2, designed to improve computational efficiency.
result Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, with slightly higher memory usage.
ROCKET speeds up time series classification without sacrificing accuracy.
problem High computational complexity and intractability of existing time series classification methods.
method Simple linear classifiers using random convolutional kernels.
result Achieves state-of-the-art accuracy with significantly reduced computational expense.
Adversarial examples fool both computer vision and humans.
problem Vulnerability of machine learning models to adversarial examples.
method Transfer adversarial examples from known models to unknown models and match human visual processing.
result Adversarial examples influence human classifications.
New algorithm boosts deep learning training speed.
problem Efficiently train deep neural networks on large clusters.
method Synchronous distributed SGD with amortized inference model.
result Dynamic cutoff improves convergence and training time.
New methods improve Reservoir Computing for chaotic time series prediction.
problem Chaotic time series prediction in Reservoir Computing.
method Established Recurrent Kernel limit, introduced Structured Reservoir Computing.
result Structured Reservoir Computing is faster and more memory-efficient.
Implicit models can match or exceed explicit models with more test-time compute.
problem Understanding the expressive power and scaling of implicit models.
method Nonparametric analysis of expressive power, mathematical characterization of implicit operators, and test-time scaling experiments.
result Implicit models can progressively express more complex mappings through iteration, matching a richer function class with test-time compute.
Quantum computers can speed up machine learning optimization problems.
problem Long computation times and high resource requirements for classical optimization algorithms in machine learning.
method Developed a mathematical model to leverage quantum parallelism for machine learning.
result Quantum machine learning applied to a 3D time-varying image demonstrated significant speedup.
Quadratic-time algorithm computes stretch factors and foliations for pseudo-Anosov mapping classes.
problem Computing stretch factors and foliations for pseudo-Anosov mapping classes efficiently.
method Quadratic-time algorithm using input word and length as complexity measure.
result First algorithm to compute stretch factors and foliations in sub-exponential time.
New method discovers time series motifs under DTW, significantly reducing computations.
problem Discovering time series motifs under DTW is computationally challenging.
method Exact scalable method using novel lower bounds hierarchy.
result Prunes up to 99.99% of DTW computations under realistic settings.
MINIROCKET speeds up time series classification by 75x.
problem High computational complexity in time series classification.
method Random convolutional kernels and linear classifier.
result Up to 75 times faster on larger datasets.
The paper proposes using path signatures for better inference in time series data.
problem Simulation models with time series data often lack tractable likelihood functions.
method Approximate Bayesian Computation with path signatures to handle sequential data.
result Theoretical guarantees on the resultant posteriors for Bayesian parameter inference.
A new method speeds up Gaussian process covariance computations.
problem Efficiently computing predictive covariance in Gaussian processes.
method Lanczos algorithm for rapid predictive covariance matrix approximation.
result 2,000 times faster computation of predictive covariances.
The study examines how verifier imperfections impact test-time scaling techniques.
problem Understanding how verifier imperfections affect test-time scaling methods.
method Proves the instance-level accuracy of Best-of-N and Rejection Sampling methods using the geometry of the verifier's ROC curve.
result RS outperforms BoN for fixed compute, but both converge to the same accuracy in the infinite-compute limit.
DTS improves inference-time alignment of diffusion models with less compute.
problem Inference-time alignment of diffusion models suffers from inaccurate value estimation and inefficient reuse of past computations.
method Diffusion Tree Sampling (DTS) uses a tree-based approach to propagate terminal rewards and iteratively refine value estimates.
result DTS produces asymptotically exact samples and matches the FID of best-performing baselines with up to 10x less compute.
This work makes neural sequence models more efficient by controlling computation.
problem Fixed compute for all examples in neural networks.
method Conditional computation to adapt compute to example complexity.
result Conditional Computation Transformer (CCT) improves efficiency and performance.
New algorithm improves inference-time alignment without reward hacking.
problem Improving quality of responses from language models with limited compute.
method Inference-time alignment, focusing on algorithm.
result Optimal performance and scaling-monotonicity of .
Improved SSC clustering with reduced computation time and accuracy.
problem Heavy computational burden in Sparse Subspace Clustering.
method RCOMP-SSC algorithm that restricts connections during OMP iterations.
result Improved clustering accuracy with reduced computational time.
Reduced reservoir size for faster edge computing.
problem Efficiently reducing computational resources for reservoir computing.
method Concatenating past or drifting states of the reservoir to the output layer.
result Reduced reservoir size up to one tenth without significant error increase.
This paper uses QUBO to train machine learning models on quantum computers.
problem Efficiently training machine learning models on quantum computers.
method Formulated three machine learning models (linear regression, SVM, k-means) as QUBO problems.
result Formulations are more efficient or equivalent in time and space complexity to classical methods.
New method detects anomalies in computing centers' logs.
problem Anomaly detection in continuously changing log data for predictive maintenance.
method Evolving granular classifiers using Fuzzy-set-Based evolving Modeling and evolving Granular Neural Network.
result Classification model prioritizes maintenance based on anomaly severity.
Real-time scene understanding solved using Approximate Bayesian Computation.
problem Predicting human actions, object poses, and pedestrian crossings from depth images.
method Bayesian error model, neural surrogates, and adaptive discretization.
result Real-time inference on real-world problems is feasible.
Ringmaster ASGD improves Asynchronous SGD's efficiency under varying worker times.
problem Suboptimal performance of Asynchronous SGD under heterogeneous worker computation times.
method Ringmaster ASGD, a novel Asynchronous SGD method with optimal time complexity.
result Ringmaster ASGD achieves optimal time complexity under arbitrary worker heterogeneity.
Linear time dynamic programming for the exact path of optimal models selected from a finite setcs.LG
New algorithm speeds up path computation for optimal models.
problem Finding the exact path of optimal models from a finite set.
method Dynamic programming approach for linear time computation.
result Dynamic programming achieves linear time for breakpoints computation.