Search Results for author: Benedikt Boecking

Found 11 papers, 5 papers with code

Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing

1 code implementation21 Apr 2022 Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay

We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing.

Contrastive Learning Language Modelling +4

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation

1 code implementation23 Mar 2022 Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski

However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances.

Generative Modeling Helps Weak Supervision (and Vice Versa)

no code implementations22 Mar 2022 Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.

Data Augmentation Image Classification

Weak Supervision for Affordable Modeling of Electrocardiogram Data

no code implementations9 Jan 2022 Mononito Goswami, Benedikt Boecking, Artur Dubrawski

We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points.

Time Series

ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS

no code implementations29 Sep 2021 Mononito Goswami, Chufan Gao, Benedikt Boecking, Saswati Ray, Artur Dubrawski

In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms.

Active Learning

End-to-End Weak Supervision

1 code implementation NeurIPS 2021 Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski

Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels.

Classification

Dependency Structure Misspecification in Multi-Source Weak Supervision Models

no code implementations18 Jun 2021 Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski

Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data.

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

1 code implementation ICLR 2021 Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

Weakly Supervised Classification

Pairwise Feedback for Data Programming

no code implementations16 Dec 2019 Benedikt Boecking, Artur Dubrawski

We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process.

Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism

1 code implementation19 Jun 2019 Maria De-Arteaga, Benedikt Boecking

After the peace agreement of 2016 with FARC, the killings of social leaders have emerged as an important post-conflict challenge for Colombia.

Applications Computers and Society

Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection

no code implementations3 Dec 2017 Kyle Hundman, Thamme Gowda, Mayank Kejriwal, Benedikt Boecking

Web-based human trafficking activity has increased in recent years but it remains sparsely dispersed among escort advertisements and difficult to identify due to its often-latent nature.

Decision Making

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