83 citations · 222 across the 15 of their papers we have counts for
16 papers
HVM-1: Large-scale video models pretrained with nearly 5000 hours of human-like video data
A. Emin Orhan
We introduce Human-like Video Models (HVM-1), large-scale video models pretrained with nearly 5000 hours of curated human-like video data (mostly egocentric, temporally extended, c…
Self-supervised learning of video representations from a child's perspective
A. Emin Orhan, Wentao Wang, Alex N. Wang +2
Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experie…
Scaling may be all you need for achieving human-level object recognition capacity with human-like visual experience
A. Emin Orhan
This paper asks whether current self-supervised learning methods, if sufficiently scaled up, would be able to reach human-level visual object recognition capabilities with the same…
Learning high-level visual representations from a child's perspective without strong inductive biases
A. Emin Orhan, Brenden M. Lake
Young children develop sophisticated internal models of the world based on their visual experience. Can such models be learned from a child's visual experience without strong induc…
Recognition, recall, and retention of few-shot memories in large language models
A. Emin Orhan
The training of modern large language models (LLMs) takes place in a regime where most training examples are seen only a few times by the model during the course of training. What…
Compositional generalization in semantic parsing with pretrained transformers
A. Emin Orhan
Large-scale pretraining instills large amounts of knowledge in deep neural networks. This, in turn, improves the generalization behavior of these models in downstream tasks. What e…
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?
A. Emin Orhan
This paper addresses a fundamental question: how good are our current self-supervised visual representation learning algorithms relative to humans? More concretely, how much "human…
Self-supervised learning through the eyes of a child
A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake
Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms…
Robustness properties of Facebook's ResNeXt WSL models
A. Emin Orhan
We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently ma…
Improving the robustness of ImageNet classifiers using elements of human visual cognition
A. Emin Orhan, Brenden M. Lake
We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the Image…
Improved memory in recurrent neural networks with sequential non-normal dynamics
A. Emin Orhan, Xaq Pitkow
Training recurrent neural networks (RNNs) is a hard problem due to degeneracies in the optimization landscape, a problem also known as vanishing/exploding gradients. Short of desig…
Skip Connections Eliminate Singularities
A. Emin Orhan, Xaq Pitkow
Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures. A completely satisfactory explana…
Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences
Eren Erdal Aksoy, Adil Orhan, Florentin Woergoetter
Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequence…
Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback
A. Emin Orhan, Wei Ji Ma
Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties as…
Neural Population Coding of Multiple Stimuli
A. Emin Orhan, Wei Ji Ma
In natural scenes, objects generally appear together with other objects. Yet, theoretical studies of neural population coding typically focus on the encoding of single objects in i…
Are Performance Limitations in Visual Short-Term Memory Tasks Due to Capacity Limitations or Model Mismatch?
A. Emin Orhan, Robert A. Jacobs
Performance limitations in visual short-term memory (VSTM) tasks have traditionally been explained in terms of resource or capacity limitations. It has been claimed, for example, t…