Search Results for author: Agnieszka Słowik

Found 12 papers, 4 papers with code

Explaining CLIP's performance disparities on data from blind/low vision users

no code implementations CVPR 2024 Daniela Massiceti, Camilla Longden, Agnieszka Słowik, Samuel Wills, Martin Grayson, Cecily Morrison

Testing 25 CLIP variants in a zero-shot classification task, we find that their accuracy is 15 percentage points lower on average for images captured by BLV users than web-crawled images.

Few-Shot Learning Zero-Shot Learning

Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation

no code implementations17 Jun 2021 Agnieszka Słowik, Léon Bottou

We show that neither DRO nor curating the training set should be construed as a complete solution for bias mitigation: in the same way that there is no universally robust training set, there is no universal way to setup a DRO problem and ensure a socially acceptable set of results.

Adversarial Robustness Fairness +1

Linear unit-tests for invariance discovery

2 code implementations22 Feb 2021 Benjamin Aubin, Agnieszka Słowik, Martin Arjovsky, Leon Bottou, David Lopez-Paz

There is an increasing interest in algorithms to learn invariant correlations across training environments.

Out-of-Distribution Generalization

Inductive Bias and Language Expressivity in Emergent Communication

1 code implementation4 Dec 2020 Shangmin Guo, Yi Ren, Agnieszka Słowik, Kory Mathewson

Referential games and reconstruction games are the most common game types for studying emergent languages.

Inductive Bias

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

no code implementations15 Oct 2020 Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.

Domain Generalization

Structural Inductive Biases in Emergent Communication

no code implementations4 Feb 2020 Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Christopher Pal

In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence.

Representation Learning Sentence

Towards Graph Representation Learning in Emergent Communication

no code implementations24 Jan 2020 Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden

Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces).

Graph Representation Learning Sentence

Spatial Graph Convolutional Networks

2 code implementations11 Sep 2019 Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.

Image Classification

Completing partial recipes using item-based collaborative filtering to recommend ingredients

1 code implementation23 Jul 2019 Paula Fermín Cueto, Meeke Roet, Agnieszka Słowik

Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet.

Collaborative Filtering Dimensionality Reduction +1

Dilated DenseNets for Relational Reasoning

no code implementations1 Nov 2018 Antreas Antoniou, Agnieszka Słowik, Elliot J. Crowley, Amos Storkey

Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning.

Relational Reasoning

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