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Found 10 papers, 7 papers with code
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees
Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks.
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch.
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution.
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task.
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS.
A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to the one established for traditional software development.
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
In our experience of working with domain experts who are using today's AutoML systems, a common problem we encountered is what we call "unrealistic expectations" -- when users are facing a very challenging task with a noisy data acquisition process, while being expected to achieve startlingly high accuracy with machine learning (ML).
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
This analysis requires in-depth understanding of the properties that connect both the transformed space and the raw feature space.
Which Model to Transfer? Finding the Needle in the Growing Haystack
Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline.
TSS: Transformation-Specific Smoothing for Robustness Certification
Moreover, to the best of our knowledge, TSS is the first approach that achieves nontrivial certified robustness on the large-scale ImageNet dataset.
