We introduce a Markovian single point process model, with random intensity regulated through a buffer mechanism and a self-exciting effect controlling the arrival stream to the buffer. The model applies the principle of the Hawkes process in which point process jumps generate a shot-noise intensity field. Unlike the Ha…
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48 results for “Streaming buffers”
We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds elements from a stream either to a solution set or to an extra buffer based on two adaptive thresholds, and improves by a final…
New algorithm optimizes linear system estimation from single trajectory.
problem Estimating LTI systems from a single trajectory.
method SGD with Reverse Experience Replay ()
result Optimal guarantees for parameter and prediction errors.
This work improves continual learning by selecting diverse samples for replay buffers.
problem Overcoming catastrophic forgetting in online continual learning.
method Formulates sample selection as a constraint reduction problem and uses gradient-based diversity maximization.
result Demonstrates improved performance compared to existing methods that rely on task boundaries.
CodeX improves DNN acceleration on FPGAs by encoding and customizing bitwidth.
problem Efficiently accelerating deep neural networks on FPGAs with limited memory.
method Nonlinear encoding, automated bitwidth customization, FPGA streaming buffers.
result Average 4.65x throughput improvement on MNIST, SVHN, CIFAR-10.
Unified study of stateful replay for streaming learning, reducing forgetting by 2-3x.
problem Catastrophic forgetting in streaming generative and predictive learning.
method Unified analysis of stateful replay for autoencoding, forecasting, and classification tasks.
result Stateful replay reduces average forgetting by a factor of 2-3 on heterogeneous multi-task streams.
Paper improves submodular streaming with better approximation, less memory, and lower complexity.
problem Maximizing submodular functions in streaming with a cardinality constraint.
method Sieve-Streaming++ with one pass, memory, and -approximation; adaptive complexity reduction.
result Achieves -approximation with memory and low adaptive complexity.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
New approach for feature evolution in streaming data with limited storage.
problem Rarely-provided labels in feature evolving streams.
method Incorporates manifold regularization and a buffer to adapt to different storage budgets.
result Preserves the performance of feature evolving learning across different storage budgets.
Secure aggregation for buffered asynchronous federated learning without TEEs.
problem Privacy and convergence in buffered asynchronous federated learning.
method Developed a new protocol (BASecAgg) that ensures privacy without TEEs by carefully designing masks.
result BASecAgg achieves similar convergence guarantees as FedBuff without TEEs.
A federated model learns shared archetypes from heterogeneous clients in continual learning.
problem Federated learning struggles with client heterogeneity and streaming distribution shifts.
method Clients encode their data as low-rank Hebbian operators, which are sent to a central server for aggregation and factorization into global archetypes.
result Improved global archetype reconstruction and associative retrieval in heterogeneous clients, drift, and novelty settings.
A simple technique improves continual learning by 50% on image datasets.
problem Challenges in training neural networks on a stream of shifting data.
method Experience Replay (ER) with five tricks to mitigate its shortcomings.
result ER, enhanced with tricks, achieves significant accuracy gains.
Study improves neural network performance in sequential learning for image classification.
problem Improving neural network performance in sequential learning for image classification.
method Evaluation of approaches for computing prequential description lengths, proposing forward-calibration and replay-streams.
result Improved description lengths for image classification datasets, outperforming previous results.
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Vi…
A new buffer system improves continual learning in RL agents by adapting to changing environments.
problem Improving RL agents' ability to learn from changing environments over time.
method Multi-timescale replay buffer combined with invariant risk minimization.
result The method shows improvement over baselines in continual learning settings.
New neural network avoids forgetting old knowledge as new data comes in.
problem Catastrophic forgetting in lifelong learning systems.
method Sequential Neural Coding Network, biologically-plausible synapse adaptation, task-executive control mimicry.
result Significantly less forgetting compared to standard neural models in experiments.
This study examines how risky investments affect insurance capital valuation.
problem Standard cost-of-capital assumptions do not account for risky investments.
method Analyzed effects of allowing buffer capital investments in risky assets.
result Decomposition of buffer capital contributions varies with riskiness.
BUZz defends images from adversarial attacks using simple transformations.
problem Adversarial attacks on deep neural networks for image classification.
method Combination of deep neural networks and simple image transformations.
result Achieves significant improvement over state-of-the-art defenses with a modest drop in clean accuracy.
Regulator allocates buffers to prevent financial contagion in networks with common assets.
problem Containment of default contagion in financial networks with common asset exposures.
method Allocates nonnegative buffer vectors under linear budget constraints to maximize default or insolvency resilience margins or minimize worst-case systemic losses.
result Exact synthesis results for buffer allocation under and uncertainty sets, showing significant gains over uniform and exposure-proportional allocations.
New method scales Bayesian inference for nonlinear SSMs using buffered stochastic gradient.
problem Inference for nonlinear, non-Gaussian SSMs is computationally challenging and particle degeneracy increases with longer series.
method Extends stochastic gradient MCMC to nonlinear SSMs using particle methods and error bounds.
result Demonstrates the importance of particle buffered stochastic gradient for long sequential data.
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization ta…
Efficiently combines autoregressive and set-based models for joint distributions.
problem Joint distributions over multiple predictions from set-based models.
method Causal autoregressive buffer that caches context and captures dependencies.
result Up to 20x faster joint sampling and density evaluation, up to 7x lower memory usage.
The paper proposes a method to learn from both simulation and real-world data.
problem Training autonomous systems in simulation and applying them to real-world environments.
method Balancing samples from simulation and real-world data using a replay buffer.
result The method achieves better performance in real-world tasks compared to training only in simulation.
New method improves continual learning by anchoring past knowledge.
problem Catastrophic forgetting in continual learning.
method Bilevel optimization to update current task knowledge while keeping past task predictions.
result Improves accuracy and forgetting metrics compared to experience replay.
Neural network tackles continual learning with neuromodulation and local error signals.
problem Catastrophic forgetting in continuous learning.
method Biologically-inspired neural architecture with local learning and neuromodulation, combined with transfer metalearning.
result Superior performance in continual learning tasks compared to other approaches.
Hierarchical GANs reduce anomaly detection costs.
problem Balancing anomaly detection accuracy and sampling costs.
method Hierarchical GANs for nonuniform sampling and buffer zones.
result Proposed GAN-based detector outperforms baseline in detection delay and average cost of error.
MER algorithm speeds up VI solving with Markovian data.
problem Solving stochastic variational inequalities with Markovian data.
method MER algorithm using multi-scale sampling from a Markovian buffer.
result Achieves faster convergence without knowing Markov chain mixing time.
Mean Reversion Pays, but Costsq-fin.TR
A mean-reverting financial instrument is optimally traded by buying it when it is sufficiently below the estimated `mean level' and selling it when it is above. In the presence of linear transaction costs, a large amount of value is paid away crossing bid-offers unless one devises a `buffer' through which the price mus…
Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimizationq-fin.PM
Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.
problem Optimizing long-term Defined Contribution plans with realistic constraints and dynamic dynamics.
method Formulates and solves bilevel optimization problems for pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization.
result Time-consistent Mean-bPoE strategies maintain investor preferences for minimum terminal wealth, unlike Mean-CVaR.
Paper proposes a new method to select memory data for online class-incremental learning.
problem Selecting which buffered images to replay for online class-incremental learning.
method Adversarial Shapley value scoring method to preserve latent decision boundaries.
result Proposed ASER method provides competitive or improved performance compared to state-of-the-art methods.
Improves on-policy RL by reusing data from multiple policies.
problem Lack of reuse of data from previous policies in on-policy RL.
method Adapts replay buffer concept to combine on- and off-policy learning.
result Method outperforms state-of-the-art on-policy RL algorithms.
DAC enhances exploration in reinforcement learning with entropy regularization.
problem Improving exploration efficiency in reinforcement learning.
method Sample-aware entropy regularization using replay buffer action distributions.
result DAC significantly outperforms existing algorithms in reinforcement learning tasks.
This work improves reinforcement learning with sparse rewards by following diverse past trajectories.
problem Challenges in reinforcement learning with sparse rewards and myopic behavior.
method Proposes a trajectory-conditioned policy to learn from a memory buffer of diverse past trajectories.
result Significantly outperforms existing methods on complex tasks with local optima.
Flexible VHDL design for multiple neural networks on FPGAs.
problem Inflexible neural network designs for FPGAs.
method Proposes a flexible VHDL structure with multiple processor groups.
result Allows training and testing of multiple neural networks on multiple FPGAs.
Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distrib…
A new method classifies multiple correlated data streams simultaneously.
problem Classifying multiple correlated data streams in practical scenarios.
method Double-Coupling Support Vector Machines (DC-SVM) considers both internal and external correlations.
result The proposed method outperforms traditional methods on artificial and real-world data streams.
A new method for estimating SW from streaming data.
problem Estimating Wasserstein distance from sample streams.
method Introducing a streaming estimator of the 1DW and applying it to all projections.
result Stream-SW achieves more accurate approximation of SW than random subsampling.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
Investigates optimal pension policies in PAYG systems with forward utility and ageing population.
problem Optimal investment and pension policies in PAYG systems with sustainability and adequacy constraints.
method Non-zero volatility forward CRRA utilities, closed-form optimal policies, detailed numerical analysis.
result Characterization of optimal policies and detailed impact analysis under various scenarios.
Proportional transaction costs present difficult theoretical problems in trading algorithm design, on account of their lack of analytical tractability. The author derives a solution of DT-NT-DT form for an arbitrary model in which the the traded asset has diffusive dynamics described by one or more stochastic risk fact…
Novel asynchronous SGD method resists Byzantine attacks without server storage.
problem Asynchronous distributed learning with Byzantine attacks and failures.
method Buffered Asynchronous SGD (BASGD) and its momentum variant (BASGDm).
result BASGD and BASGDm resist non-omniscient and omniscient attacks without server storage.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
problem Improving channel attention mechanisms in neural networks.
method Proposes tiled squeeze-and-excite (TSE) framework for channel attention.
result Local context of 7 rows or columns is sufficient for matching global context performance.
Improved diffusion models for sampling from given distributions.
problem Training diffusion models to sample from a given distribution.
method Benchmarked and improved off-policy methods for diffusion sampling.
result A novel exploration strategy improves sample quality.
Data stream clustering tackles real-time data processing challenges.
problem Real-time processing of data streams with less prior information.
method Review of data stream clustering algorithms and their characteristics.
result Comparison and analysis of data stream clustering algorithms.
Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions---delivery failures, faulty parts, delays, power outages, theft, government failures---that systematically thwart the product…
Bayesian tensor train method recovers streaming data with high accuracy.
problem Recovering high-order, incomplete, and noisy streaming data.
method Bayesian tensor train decomposition using streaming variational Bayes method.
result The proposed SPTT algorithm excels in recovering streaming data compared to state-of-the-art methods.
stream-learn is a Python library for analyzing data streams with various drift types.
problem Analyzing drifting and imbalanced data streams.
method Synthetic data stream generator, evaluation methodologies, and imbalanced binary classification metrics.
result Efficient implementation of classifiers for data stream analysis.
LiDER refreshes past experiences in RL by dreaming about them.
problem Improving data efficiency in off-policy RL algorithms.
method Refreshing past experiences in a replay buffer using the current policy.
result LiDER consistently improves performance in Atari games.