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Found 10 papers, 6 papers with code
Decentralized Personalized Online Federated Learning
Therefore, we propose a new learning setting \textit{Decentralized Personalized Online Federated Learning} that considers all the three aspects at the same time.
Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data
In this vision paper, we propose a shift in perspective for improving the effectiveness of similarity search.
Ground Truth Inference for Weakly Supervised Entity Matching
We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.
Learning Hyper Label Model for Programmatic Weak Supervision
We train the model on synthetic data generated in the way that ensures the model approximates the analytical optimal solution, and build the model upon Graph Neural Network (GNN) to ensure the model prediction being invariant (or equivariant) to the permutation of LFs (or data points).
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
We derive conditions of the learning framework under which the learned model is workload agnostic, in the sense that the model/estimator can be trained with synthetically generated training data, and then deployed into any data warehouse simply as, e. g., user-defined functions (UDFs), to offer efficient (within microseconds on CPU) and accurate NDV estimations for unseen tables and workloads.
Demonstration of Panda: A Weakly Supervised Entity Matching System
Panda's IDE includes many novel features purpose-built for EM, such as smart data sampling, a builtin library of EM utility functions, automatically generated LFs, visual debugging of LFs, and finally, an EM-specific labeling model.
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data.
Real Time Pattern Matching with Dynamic Normalization
Pattern matching in time series data streams is considered to be an essential data mining problem that still stays challenging for many practical scenarios.
Databases Multimedia
ZeroER: Entity Resolution using Zero Labeled Examples
We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches?
GOGGLES: Automatic Image Labeling with Affinity Coding
We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set.
