arXiv Machine Learning
Jun 30
Meta-learning as a principle for human-like visual representations
arXiv:2606. 28399v1 Announce Type: cross Abstract: The structure of human visual representations underpins our capacity for adaptive behaviour.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
arXiv:2109. 13445v3 Announce Type: replace-cross Abstract: The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood.
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arXiv AI
Jul 2
Identifying Latent Concepts and Structures for Generalized Category Discovery
arXiv:2607. 00620v1 Announce Type: cross Abstract: Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings.
arXiv AI
Jun 19
Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.
arXiv AI
Jul 7
Human-like Object Grouping in Self-supervised Vision Transformers
arXiv:2603. 13994v2 Announce Type: replace-cross Abstract: Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties.
arXiv Machine Learning
Jul 10
Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks
arXiv:2607. 08561v1 Announce Type: new Abstract: A series of results from the NeuroAI over the past fifteen years have raised core questions both about how to compare Deep Neural Network (DNN) models to the brain, and about how much convergent evolution to expect between artificial networks and real brain networks.
arXiv AI
Jun 16
Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE
arXiv:2606. 15989v1 Announce Type: cross Abstract: Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders.
arXiv Machine Learning
Jul 14
Emergent Generalization by Representation Learning in Artificial Neural Networks
arXiv:2607. 10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity.
arXiv AI
Jun 16
A biological vision inspired framework for machine perception of abutting grating illusory contours
arXiv:2508. 17254v2 Announce Type: replace-cross Abstract: Higher levels of machine intelligence demand alignment with human perception and cognition.
arXiv Machine Learning
Jul 7
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
arXiv:2602. 24264v2 Announce Type: replace-cross Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems.
arXiv AI
Jun 2
Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
arXiv:2505. 20853v3 Announce Type: replace-cross Abstract: Fusing heterogeneous information remains a persistent challenge in modern data analysis.
OpenAI Blog
Mar 4, 2021
Multimodal neurons in artificial neural networks
We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn.
arXiv AI
2d ago
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
arXiv:2502. 14424v3 Announce Type: replace-cross Abstract: Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified.