Quantum algorithm improves generative models exponentially.
problem Finding efficient quantum algorithms for generative machine learning.
method Proposes a quantum generative model with exponential speedup.
result Exponential speedup in training and inference for some instances.
System learns optimizer hyperparameters to generalize across tasks.
problem Lack of generalization in learning optimizers for neural networks.
method Generalization-first approach, learning optimizer hyperparameters.
result System outperforms Adam on various tasks, including unseen modalities.
New insights explain speedup saturation in distributed learning with large batches and delays.
problem Understanding and optimizing speedup in distributed learning with large batches and delays.
method Theoretical analysis of strongly convex, convex, and non-convex settings, considering data sparsity.
result Identification of a data-dependent parameter explaining speedup saturation in both batch size and gradient staleness.
ProxSkip achieves linear speedup in distributed non-convex optimization.
problem Achieving linear speedup in distributed non-convex optimization.
method Unified convergence analysis for stochastic non-convex, convex, and strongly convex problems.
result ProxSkip achieves linear speedup in the number of nodes under stochastic gradients.
Linear speedup achieved in non-convex optimization for decentralized systems.
problem Achieving optimal performance in decentralized non-convex optimization.
method Examined the dependence of convergence guarantees on spectral properties of combination policies.
result Linear speedup in saddle-point escape time for symmetric combination policies.
Paper analyzes convergence and speedup of asynchronous parallel SGD.
problem Achieving good convergence and linear speedup in asynchronous parallel SGD.
method Second-order convergence analysis of APSGD with consistent read near strictly saddle points.
result Theoretical guarantee for using at most O ( K 1 / 3 M − 1 / 3 ) O(K^{1/3}M^{-1/3}) O ( K 1/3 M − 1/3 ) workers for good convergence and linear speedup. Paper speeds up structured output prediction without sacrificing accuracy.
problem Computational inefficiency in predicting structured outputs.
method Learning to search approach to train a speedup classifier.
result Speedup classifier outperforms greedy search in terms of speed.
Quantum computing promises faster finance algorithms.
problem Solving finance problems faster than classical methods.
method Quantum computing applications to finance, including Monte Carlo, portfolio optimization, and machine learning.
result Quantum speedups for finance problems, especially Monte Carlo and portfolio optimization.
A fast method for Lasso and Logistic Lasso problems.
problem Solving Lasso and Logistic Lasso regression problems efficiently.
method Iterative active set approach using solver updates.
result 31.41 times faster on average for compressed sensing.
Quantum algorithms speed up derivative pricing beyond Black-Scholes models.
problem Quantum speedups for derivative pricing beyond Black-Scholes models.
method Utilizing fast-forwardability and quantum Milstein sampler for non-GBM models, and improved numerical integration for GBM and CIR models.
result Quadratic speedups for derivative pricing in practical models like CIR and Heston's model.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
problem Estimating non-linear functionals of probability distributions.
method Proposes a quantum-inside-quantum Monte Carlo algorithm for a broad class of non-linear estimation problems.
result Achieves a quadratic speedup for non-linear estimation problems, including nested conditional expectations and stochastic optimization.
ML-EM method speeds up diffusion model sampling.
problem Efficiently sampling from complex diffusion models.
method Multilevel Euler-Maruyama method with UNet approximations.
result Polynomial speedup in sampling from diffusion models.
Parallelizes LARS for high-dimensional data with speedups and accuracy trade-offs.
problem Fitting linear regression models to high-dimensional data efficiently.
method Two parallel and communication avoiding versions of LARS: bLARS and Tournament-bLARS.
result Speedups up to 4x compared to LARS, with trade-offs in solution quality.
Quantum algorithm speeds up nested expectation estimation by nearly quadratically.
problem Estimating repeatedly nested expectations with quantum computing.
method Proposes a quantum algorithm achieving nearly quadratic speedup over classical methods.
result Achieves nearly quadratic speedup for RNEs, up to logarithmic factors.
Federated Q-Learning achieves linear regret speedup with low communication cost.
problem Achieving linear regret speedup in federated reinforcement learning without high communication costs.
method Proposed two federated Q-Learning algorithms: FedQ-Hoeffding and FedQ-Bernstein, using event-triggered synchronization, novel step size selection, and concentration inequalities.
result Total regrets achieve linear speedup compared to single-agent counterparts with logarithmic communication cost.
A new algorithm speeds up deep learning training by decoupling computation and communication.
problem High communication cost limits the speedup of distributed SGD.
method CoCoD-SGD: runs computation and communication in parallel.
result Linear time speedup with respect to hardware resources.
GPU optimization speeds up large-scale classification tasks.
problem Efficiently training large-scale classification models on GPUs.
method Judecious GPU-optimization principles applied to TRON algorithm.
result Significant speedups for logistic regression and SVM classification.
Quantum algorithm speeds up MIP solving by a near-quadratic factor.
problem Solving Mixed Integer Programs (MIPs) efficiently.
method Incremental-Quantum-Branch-and-Bound algorithm combining quantum speedup with classical search heuristics.
result Universal near-quadratic speedup over classical Branch-and-Bound algorithms.
A method speeds up generation in convolutional autoregressive models.
problem Slow generation in convolutional autoregressive models.
method Cache hidden states to avoid redundant computation.
result Up to 21x and 183x speedups in generation for Wavenet and PixelCNN++ models.
WU-UCT parallelizes MCTS with linear speedup and limited performance loss.
problem Challenges in parallelizing Monte Carlo Tree Search (MCTS) due to its sequential nature.
method Introduces unobserved samples to track incomplete simulations and modify UCT tree policy.
result Achieves linear speedup and only limited performance loss with increasing parallel workers.
Optimized parallel RNN training reaches up to 845x speedup.
problem Expensive RNN training through back-propagation through time (BPTT).
method Optimized parallel algorithm \opt based on ELM, leveraging GPU shared memory and QR factorization.
result Up to 845x speedup over sequential training and 20x less time to train.
Neural network speeds up atmospheric chemistry modeling 4250x.
problem Computational expense of simulating atmospheric chemistry.
method Created a neural network to emulate a complex chemical mechanism.
result Achieved a 250x computational speedup.
New algorithm achieves linear speedup in non-i.i.d. federated bilevel learning.
problem Linear speedup in convergence for non-i.i.d. datasets in federated bilevel optimization.
method Proposes FedMBO with a novel client sampling scheme for non-i.i.d. datasets.
result Achieves a convergence rate of O(1/√(nK) + 1/K + √n/K³/²).
Efficiently approximates Sparse PCA with significant speedups and minor error.
problem Sparse Principal Component Analysis (Sparse PCA) is NP-hard and computationally expensive.
method Approximates the covariance matrix with block-diagonal form, solves sub-problems in each block, and reconstructs the solution.
result Significant computational speedups with minor additive error.
ECD algorithm speeds up non-convex optimization, offering quantum and stochastic enhancements.
problem Non-convex optimization challenges in machine learning.
method Energy Conserving Descent (ECD) algorithm, stochastic ECD dynamics (sECD), quantum ECD Hamiltonian (qECD).
result ECD and its quantum version achieve exponential speedup over gradient descent.
Unified analysis of Federated Averaging and Nesterov FedAvg for linear speedup.
problem Understanding convergence of FL algorithms under non-i.i.d. data and partial participation.
method Systematic study of convergence guarantees for FedAvg and Nesterov FedAvg under different conditions.
result Unified analysis of linear speedup for FedAvg and Nesterov FedAvg in various settings.
CHEETAH speeds up secure MLaaS by 100x over fastest existing schemes.
problem Privacy-preserving machine learning on end devices.
method Ultra-fast secure MLaaS framework using secret sharing.
result More than 100x speedup over fastest existing schemes.
Investigates the impact of batch size on GPU and TPU performance.
problem Optimizing performance of GPUs and TPUs during training and inference phases.
method Investigated the impact of batch size on performance of GPUs and TPUs using standard MNIST and Fashion-MNIST datasets.
result Significant speedup was achieved even with low-scale usage of TPUv2 units, up to 10x for training and 2x for prediction.
We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework. Whenever possible, we perform computations asynchronously, which helps attain speedups on multicore machines as well as in distributed environments. Moreover…
Novel periodic momentum SGD method for decentralized training with linear speedup.
problem Lack of effective momentum schema in decentralized training methods.
method Proposes a novel periodic decentralized momentum SGD method.
result Achieves linear speedup in decentralized training.
Speed up neural networks by 2x with 5% mAP loss.
problem High computational cost of neural network forward passes.
method Deep Learning Approximation: lossless and lossy optimizations.
result 2x speedup in network forward pass with 5% mAP drop.
Quantum algorithm speeds up learning from big data exponentially.
problem Scalable learning from big data with optimized random features.
method Quantum algorithm for sampling optimized random features.
result Exponential speedup in runtime compared to classical algorithms.
Weight normalization speeds up matrix sensing problems.
problem Matrix sensing with overparameterization.
method Generalized weight normalization with Riemannian optimization.
result WN achieves linear convergence, improving speed and complexity.
Proposes a faster algorithm for machine learning problems.
problem General minimum conical hull problems in machine learning.
method Sublinear classical algorithm for general minimum conical hull problems.
result Achieves exponential speedup over existing methods.
RNN operators solve Newton's equations with large timesteps for molecular dynamics.
problem Solving Newton's equations of motion with large timesteps for molecular dynamics simulations.
method Recurrent Neural Networks (RNN) operators to solve Newton's equations using past trajectory data.
result Significant speedup in molecular dynamics simulations with timesteps up to 4000 times larger.
Speeds up deep neural networks training by 10x using GPU concurrency.
problem Training deep residual neural networks efficiently.
method Layer-wise parallel training with GPU concurrency and Nonlinear Multigrid.
result 10.2x speedup over traditional techniques.
Federated learning algorithm improves with intermittent client availability.
problem Performance degradation in Federated Averaging due to client availability changes.
method Federated Latest Averaging (FedLaAvg) uses latest gradients from all clients, even when unavailable.
result FedLaAvg achieves sublinear speedup compared to classical Federated Averaging.
Paper studies collaboration in multi-armed bandits with limited interaction.
problem Identifying the best arm collaboratively with limited communication.
method Developed techniques to quantify and prove round-speedup tradeoffs.
result Almost tight round-speedup tradeoffs for distributed exploration.
SSL method reduces DNN computation and improves accuracy.
problem Resource constraints in deploying large-scale DNNs.
method Structured Sparsity Learning (SSL) to regularize DNN structures.
result SSL achieves significant speedups and accuracy improvements.
SCNN improves video object detection speed by 178%.
problem Limited throughput in existing CNNs for video object detection.
method Proposes SCNN, a statistical CNN that processes correlated distributions.
result Achieves 178% speedup over existing CNNs for video object detection.
CompactNet optimizes CNN models for resource-limited platforms.
problem Challenges in implementing CNN models on resource-limited platforms.
method Guided by a simulator, CompactNet progressively trims a pre-trained network to achieve target speedup while maintaining accuracy.
result Achieves up to 1.8x kernel computation speedup on embedded platforms.
Paper presents an efficient algorithm for learning minimax risk classifiers with large-scale data.
problem Efficient learning of minimax risk classifiers for large-scale data with multiple classes.
method Combination of constraint and column generation for efficient learning.
result 10x speedup for general large-scale data and 100x speedup with many classes.
A simple approach speeds up probabilistic programming models.
problem Efficiently implementing probabilistic programming in deep learning.
method Embedding probabilistic programming into TensorFlow with a single random variable abstraction.
result Optimal linear speedup from 1 to 256 TPUv2 chips, and 100x speedup on GPUs.
CYCLADES speeds up machine learning with conflict-free asynchronous updates.
problem Improving parallel stochastic optimization in shared memory systems.
method Asynchronous parallelization without memory locking, introducing conflict-free updates.
result Consistently outperforms HOGWILD!-type algorithms, achieving up to 5x speedup gains.
Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations …
The paper introduces a multi-scale model to speed up sparse approximation problems for visual signals.
problem Efficiently solving sparse approximation problems with large dictionaries.
method Incorporates multi-scale structure onto dictionary-based sparse representations.
result Significant speedups (10-60x) with little loss in accuracy for images, videos, and light fields.
Asynchronous L-BFGS speeds up non-convex optimization.
problem Non-convex optimization challenges in machine learning.
method Asynchronous stochastic L-BFGS algorithm for non-convex optimization.
result Achieves an ergodic convergence rate of O ( 1 / N ) {\cal O}(1/\sqrt{N}) O ( 1/ N ) and linear speedup. We parallelize backpropagation for deep learning models, achieving significant speedups.
problem Sequential dependency in backpropagation limits scalability on parallel systems.
method Reformulated backpropagation as a scan operation, using Blelloch scan algorithm.
result Up to 2.75x speedup on overall training time and 108x on backward pass.