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.
Automated EEG analysis gauges mental workload in task evaluation.
Paper develops a BERT-based classifier to reduce pathology report annotation workload.
AutoPQ automates quantile forecasting for smart grids, reducing workload and environmental impact.
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
SliceOut speeds up deep learning training without sacrificing accuracy.
KineticSim: A lightweight, high-performance execution engine for real-time market simulators
The paper predicts workload using process mining and neural networks.
Developing reliable workload predictive models can affect many aspects of clinical decision making procedure. The primary challenge in healthcare systems is handling the demand uncertainty over the time. This issue becomes more critical for the healthcare facilities that provide service for chronic disease treatment be…
Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD, often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements are difficult to satisfy when the paral…
AutoYara generates effective Yara rules faster than humans.
Systematic reviews, which summarize and synthesize all the current research in a specific topic, are a crucial component to academia. They are especially important in the biomedical and health sciences, where they synthesize the state of medical evidence and conclude the best course of action for various diseases, path…
KineticSim accelerates financial market simulations 3406x over CPU.
This work introduces a new benchmark to compare neural network training algorithms.
Proposes a framework for energy-efficient AIGC workload scheduling in cloud data centers.
A new method reduces data movement in neural network training.
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern ma…
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists w…
This thesis explores fast algorithms for large matrices and data augmentation to improve model efficiency.
Paper proposes a method to predict optimal data partitioning based on query execution costs.
New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.
Assessment of mental workload in real-world conditions is key to ensure the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across subjects and physical s…
Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some targeted workloads. We propose a novel framework for privacy preservation based on the …
Simple policy outperforms complex ones in cloud auto-scaling.
SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.
Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configu…
This paper optimizes how deep learning models are distributed across different devices.
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive wor…
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we …
AlgoPerf competition evaluates neural network training speed-ups.
Optimizes resource allocation for virtualized network functions based on performance profiles.
Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.
The Long-Short-Term-Memory Recurrent Neural Networks (LSTM RNNs) are a popular class of machine learning models for analyzing sequential data. Their training on modern GPUs, however, is limited by the GPU memory capacity. Our profiling results of the LSTM RNN-based Neural Machine Translation (NMT) model reveal that fea…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise the…
FLAME auto-labels mobile data efficiently on diverse processors.
We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems. However, existing systems rely on manually optimized librar…
Service-induced congestion in memory-constrained LLM serving
We improve private training accuracy with learning rate schedules and matrix factorizations.
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user's dataset is prohibitively expensive. Therefo…
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for each workload/architecture, leading to numerous, complex code-bases that strive for performance, yet …
A new method for private query release using Johnson-Lindenstrauss projection.
Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch gradient methods. But the efficacy of such ideas for mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm of moder…
Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally charac…
Stage-based hyper-parameter optimization reduces GPU-hours and training time.
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. We present Sage, a differentially private (DP) ML platform that bounds the cumulative leakage of training data through models. Sage builds upon the ric…
XLabel tool reduces medical experts' workload by 40% and explains its decisions.
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
Improved neural network training in low-dimensional random bases.
Equity-Transformer solves NP-hard min-max routing problems efficiently.