Search Results for author:
Found 29 papers, 8 papers with code
Continual Learning with Low Rank Adaptation
Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest.
Renate: A Library for Real-World Continual Learning
We discuss requirements for the use of continual learning algorithms in practice, from which we derive design principles for Renate.
Variational Boosted Soft Trees
Variational inference is often used to implement Bayesian neural networks, but is difficult to apply to GBMs, because the decision trees used as weak learners are non-differentiable.
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run.
Continual Learning with Transformers for Image Classification
This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used.
Memory Efficient Continual Learning with Transformers
Moreover, applications increasingly rely on large pre-trained neural networks, such as pre-trained Transformers, since the resources or data might not be available in sufficiently large quantities to practitioners to train the model from scratch.
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods.
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evaluate hyperparameter configurations.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Arguably their most significant impact has been in image classification and object detection tasks where the state of the art results have been obtained.
Hardware Aware Neural Architecture Search
Image Classification
+3
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogate is learned to approximate the black box response function (e. g. validation error).
Learning to Rank Learning Curves
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
XferNAS: Transfer Neural Architecture Search
In experiments on CIFAR-10 and CIFAR-100, we observe a reduction in the search time from 200 to only 6 GPU days, a speed up by a factor of 33.
A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search.
Inductive Transfer for Neural Architecture Optimization
First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Automated Image Data Preprocessing with Deep Reinforcement Learning
Data preparation, i. e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task.
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians.
Neural Feature Learning From Relational Database
To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.
Finding Competitive Network Architectures Within a Day Using UCT
We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree.
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Deep Learning models are vulnerable to adversarial examples, i. e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence.
Automatic Frankensteining: Creating Complex Ensembles Autonomously
Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners.
Bank Card Usage Prediction Exploiting Geolocation Information
We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery Challenge on Bank Card Usage for both tasks.
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts
In this paper, we use products of Gaussian process experts as surrogate models for hyperparameter optimization.
Ultra-Fast Shapelets for Time Series Classification
A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains.
Scalable Discovery of Time-Series Shapelets
Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data.
Time-Series Classification Through Histograms of Symbolic Polynomials
The coefficients of the polynomial functions are converted to symbolic words via equivolume discretizations of the coefficients' distributions.
