Search Results for author:
Found 31 papers, 13 papers with code
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class
We propose a method to encode and account for diversity within a class using inferred attributes, still in the zero-shot setting without retraining.
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data.
Discovering environments with XRM
Successful out-of-distribution generalization requires environment annotations.
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation.
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?
Second, we study model generalization across geographies by measuring the disparities in performance across regions, a more fine-grained measure of real world generalization.
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level.
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Equipped with ImageNet-X, we investigate 2, 200 current recognition models and study the types of mistakes as a function of model's (1) architecture, e. g. transformer vs. convolutional, (2) learning paradigm, e. g. supervised vs. self-supervised, and (3) training procedures, e. g., data augmentation.
The Robustness Limits of SoTA Vision Models to Natural Variation
We study not only how robust recent state-of-the-art models are, but also the extent to which models can generalize variation in factors when they're present during training.
Robust Self-Supervised Learning with Lie Groups
Our approach applies the formalism of Lie groups to capture continuous transformations to improve models' robustness to distributional shifts.
Disentanglement of Correlated Factors via Hausdorff Factorized Support
We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over $+60\%$ in relative improvement over existing disentanglement methods.
Measuring and signing fairness as performance under multiple stakeholder distributions
By highlighting connections to the literature in domain generalization, we propose to measure fairness as the ability of the system to generalize under multiple stress tests -- distributions of examples with social relevance.
Grounding inductive biases in natural images:invariance stems from variations in data
While prior work has focused on synthetic data, we attempt here to characterize the factors of variation in a real dataset, ImageNet, and study the invariance of both standard residual networks and the recently proposed vision transformer with respect to changes in these factors.
Grounding inductive biases in natural images: invariance stems from variations in data
While prior work has focused on synthetic data, we attempt here to characterize the factors of variation in a real dataset, ImageNet, and study the invariance of both standard residual networks and the recently proposed vision transformer with respect to changes in these factors.
Addressing the Topological Defects of Disentanglement via Distributed Operators
A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e. g. object shape vs. pose).
Addressing the Topological Defects of Disentanglement
A core challenge in Machine Learning is to disentangle natural factors of variation in data (e. g. object shape vs pose).
Think before you act: A simple baseline for compositional generalization
Contrarily to humans who have the ability to recombine familiar expressions to create novel ones, modern neural networks struggle to do so.
Permutation Equivariant Models for Compositional Generalization in Language
Humans understand novel sentences by composing meanings and roles of core language components.
Compositionality and Generalization in Emergent Languages
Third, while compositionality is not necessary for generalization, it provides an advantage in terms of language transmission: The more compositional a language is, the more easily it will be picked up by new learners, even when the latter differ in architecture from the original agents.
A Benchmark for Systematic Generalization in Grounded Language Understanding
In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding.
Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game
Research in multi-agent cooperation has shown that artificial agents are able to learn to play a simple referential game while developing a shared lexicon.
EDUCE: Explaining model Decision through Unsupervised Concepts Extraction
Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input.
Mastering emergent language: learning to guide in simulated navigation
A typical setup to achieve this is with a scripted teacher which guides a virtual agent using language instructions.
EGG: a toolkit for research on Emergence of lanGuage in Games
There is renewed interest in simulating language emergence among deep neural agents that communicate to jointly solve a task, spurred by the practical aim to develop language-enabled interactive AIs, as well as by theoretical questions about the evolution of human language.
Entropy Minimization In Emergent Languages
There is growing interest in studying the languages that emerge when neural agents are jointly trained to solve tasks requiring communication through a discrete channel.
Miss Tools and Mr Fruit: Emergent communication in agents learning about object affordances
Recent research studies communication emergence in communities of deep network agents assigned a joint task, hoping to gain insights on human language evolution.
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input.
How agents see things: On visual representations in an emergent language game
There is growing interest in the language developed by agents interacting in emergent-communication settings.
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control.
DISCO Nets : DISsimilarity COefficients Networks
We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets).
DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets).
Entropy-Based Latent Structured Output Prediction
To this end, we propose a novel prediction criterion that includes as special cases all previous prediction criteria that have been used in the literature.
