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Found 16 papers, 1 papers with code
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers).
Improving Transferability of Deep Neural Networks
This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source dataset.
Automatic Labeling of Data for Transfer Learning
Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset.
P2L: Predicting Transfer Learning for Images and Semantic Relations
We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.
FfDL : A Flexible Multi-tenant Deep Learning Platform
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
Improving the affordability of robustness training for DNNs
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models.
Sequential Drift Detection in Deep Learning Classifiers
We utilize neural network embeddings to detect data drift by formulating the drift detection within an appropriate sequential decision framework.
Detection of data drift and outliers affecting machine learning model performance over time
Drift is distribution change between the training and deployment data, which is concerning if model performance changes.
Adversarial training in communication constrained federated learning
We observe a significant drop in both natural and adversarial accuracies when AT is used in the federated setting as opposed to centralized training.
Machine Learning Model Drift Detection Via Weak Data Slices
Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge.
Using sequential drift detection to test the API economy
It is thus desirable to monitor the usage patterns and identify when the system is used in a way that was never used before.
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning
Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited.
Accelerating Parallel Stochastic Gradient Descent via Non-blocking Mini-batches
In this paper, we propose the (de)centralized Non-blocking SGD (Non-blocking SGD) which can address the straggler problem in a heterogeneous environment.
RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning
The convergence of SGD based distributed training algorithms is tied to the data distribution across workers.
Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level
The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism.
