Theodoros Rekatsinas | DeepAI

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∙ 05/28/2023

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning

Methods for carefully selecting or generating a small set of training da...

0 Patrik Okanovic, et al. ∙

∙ 05/16/2023

Growing and Serving Large Open-domain Knowledge Graphs

Applications of large open-domain knowledge graphs (KGs) to real-world p...

0 Ihab F. Ilyas, et al. ∙

∙ 04/04/2023

High-Throughput Vector Similarity Search in Knowledge Graphs

There is an increasing adoption of machine learning for encoding data in...

0 Jason Mohoney, et al. ∙

∙ 04/15/2022

Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale

We introduce Saga, a next-generation knowledge construction and serving ...

13 Ihab F. Ilyas, et al. ∙

∙ 02/04/2022

Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine

Graph Neural Networks (GNNs) have emerged as a powerful model for ML ove...

0 Roger Waleffe, et al. ∙

∙ 06/02/2021

Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins

Structured data, or data that adheres to a pre-defined schema, can suffe...

13 Sahaana Suri, et al. ∙

∙ 01/20/2021

Marius: Learning Massive Graph Embeddings on a Single Machine

We propose a new framework for computing the embeddings of large-scale g...

0 Jason Mohoney, et al. ∙

∙ 10/14/2020

Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion

We show that state-of-the-art self-supervised language models can be rea...

0 Ankur Goswami, et al. ∙

∙ 06/23/2020

Principal Component Networks: Parameter Reduction Early in Training

Recent works show that overparameterized networks contain small subnetwo...

0 Roger Waleffe, et al. ∙

∙ 06/18/2020

Record fusion: A learning approach

Record fusion is the task of aggregating multiple records that correspon...

9 Alireza Heidari, et al. ∙

∙ 06/08/2020

Picket: Self-supervised Data Diagnostics for ML Pipelines

Data corruption is an impediment to modern machine learning deployments....

0 Zifan Liu, et al. ∙

∙ 06/06/2020

An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation

Generalization Performance of Deep Learning models trained using the Emp...

0 Deepan Das, et al. ∙

∙ 02/10/2020

Robust Mean Estimation under Coordinate-level Corruption

Data corruption, systematic or adversarial, may skew statistical estimat...

4 Zifan Liu, et al. ∙

∙ 10/28/2019

Fine-Grained Object Detection over Scientific Document Images with Region Embeddings

We study the problem of object detection over scanned images of scientif...

0 Ankur Goswami, et al. ∙

∙ 06/29/2019

Approximate Inference in Structured Instances with Noisy Categorical Observations

We study the problem of recovering the latent ground truth labeling of a...

5 Alireza Heidari, et al. ∙

∙ 05/30/2019

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Directed graphical models (DGMs) are a class of probabilistic models tha...

1 Amrita Roy Chowdhury, et al. ∙

∙ 05/04/2019

Learning Functional Dependencies with Sparse Regression

We study the problem of discovering functional dependencies (FD) from a ...

0 Zhihan Guo, et al. ∙

∙ 04/04/2019

HoloDetect: Few-Shot Learning for Error Detection

We introduce a few-shot learning framework for error detection. We show ...

0 Alireza Heidari, et al. ∙

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

∙ 01/21/2018

A Formal Framework For Probabilistic Unclean Databases

Traditional modeling of inconsistency in database theory casts all possi...

0 Christopher De Sa, et al. ∙