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.
Amazon SageMaker AMT automates machine learning model tuning.
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
Random Forests automatically prune a latent 'true' tree, explaining their overfitting without tuning.
New method improves hyperparameter tuning efficiency across similar tasks.
Proposes an automatic cyclical scheduling for gradient-based discrete sampling.
Meta-SAC automatically tunes SAC's entropy temperature for better exploration.
MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impact on large-scale machine learning (ML) performance. Traditionally, these tunables are set manually, which is unsurprisingly error-prone and…
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely bas…
AutoInit automatically finds good neural network initialization.
Autotune optimizes Lasso tuning parameters efficiently and accurately.
AutoSGD automatically adjusts learning rates for SGD.
D-Adaptation automatically sets optimal learning rates without manual tuning.
Interactive applications incorporating high-data rate sensing and computer vision are becoming possible due to novel runtime systems and the use of parallel computation resources. To allow interactive use, such applications require careful tuning of multiple application parameters to meet required fidelity and latency …
BASS efficiently learns time-varying graphs with low complexity and automatic tuning.
Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of deve…
New method improves sampling from complex, multi-peaked distributions.
Tuning SVM and boosting models using optimization algorithms.
We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint. We propose to solve it as a sequential decision making problem, such that we can use the partial training progress of configurations to dynamically allocate the remaining budget. Our algorithm combi…
GOLS-I automatically determines learning rates for various neural network training algorithms.
We introduce a technique that can automatically tune the parameters of a rule-based computer vision system comprised of thresholds, combinational logic, and time constants. This lets us retain the flexibility and perspicacity of a conventionally structured system while allowing us to perform approximate gradient descen…
AutoGD automatically adjusts learning rates for gradient descent.
Stochastic Gradient Descent (SGD) methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment of hyperparameters is very costly and time-consuming, and e…
A new Mapper algorithm optimizes data visualization through automatic parameter tuning.
Recent studies have shown that tuning prediction models increases prediction accuracy and that Random Forest can be used to construct prediction intervals. However, to our best knowledge, no study has investigated the need to, and the manner in which one can, tune Random Forest for optimizing prediction intervals { thi…
DoWG optimizer automatically adapts to convex and nonsmooth problems without tuning.
Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time. A common but trivial approach is to train individual binary classifiers per label, but the performance can be improved by considering associations within the labels. Like with any machine learnin…
New methods reduce private federated learning communication automatically.
AutoML-GPT uses GPT to automate AI model training.
During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvements in playing strength. For example, prior to the match with Lee Sedol, we tuned the latest AlphaGo agent and this improved its win-rate from…
New method for hyperparameter tuning in sparse matrix factorization.
Novel approach trains ASR models with less supervision using bilevel optimization.
A new method for automatic gradient tree boosting using information theory.
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Rec…
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models f…
This work improves texture segmentation by automatically tuning hyperparameters for Total-Variation.
ARDA automatically augments datasets for machine learning models.
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples tha…
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. Th…
Bayesian TNKMs automatically infer model complexity and feature relevance.
Three methods for tuning HMC diagonal scale matrices compared.
ICE-Pruning accelerates deep neural network pruning by 9.61x.
Autodock is a widely used molecular modeling tool which predicts how small molecules bind to a receptor of known 3D structure. The current version of AutoDock uses meta-heuristic algorithms in combination with local search methods for doing the conformation search. Appropriate settings of hyperparameters in these algor…
A framework to boost the efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. We call it the refined variational approximation. Its strength lies both in ease of implementation and automatically tuning of the sampler parameters to …
Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives. In this paper, we address the p…
ControlVAE improves VAE performance by adding a controller to tune hyperparameters.
In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique ch…
CL methods improve monolingual ASR models across new tasks without forgetting past data.