412 citations · 919 across the 20 of their papers we have counts for
22 papers
BrainBERT: Self-supervised representation learning for intracranial recordings
Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari +4
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition…
What can human minimal videos tell us about dynamic recognition models?
Guy Ben-Yosef, Gabriel Kreiman, Shimon Ullman
In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly under…
Fooling the primate brain with minimal, targeted image manipulation
Li Yuan, Will Xiao, Giorgia Dellaferrera +4
Artificial neural networks (ANNs) are considered the current best models of biological vision. ANNs are the best predictors of neural activity in the ventral stream; moreover, rece…
Simple Learning Rules Generate Complex Canonical Circuits
Joseph Olson, Gabriel Kreiman
Cortical circuits are characterized by exquisitely complex connectivity patterns that emerge during development from undifferentiated networks. The development of these circuits is…
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Mengmi Zhang, Gabriel Kreiman
Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We…
Can Deep Learning Recognize Subtle Human Activities?
Vincent Jacquot, Zhuofan Ying, Gabriel Kreiman
Deep Learning has driven recent and exciting progress in computer vision, instilling the belief that these algorithms could solve any visual task. Yet, datasets commonly used to tr…
Frivolous Units: Wider Networks Are Not Really That Wide
Stephen Casper, Xavier Boix, Vanessa D'Amario +4
A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests…
Putting visual object recognition in context
Mengmi Zhang, Claire Tseng, Gabriel Kreiman
Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g., a…
Variational Prototype Replays for Continual Learning
Mengmi Zhang, Tao Wang, Joo Hwee Lim +2
Continual learning refers to the ability to acquire and transfer knowledge without catastrophically forgetting what was previously learned. In this work, we consider \emph{few-shot…
Gradient-free activation maximization for identifying effective stimuli
Will Xiao, Gabriel Kreiman
A fundamental question for understanding brain function is what types of stimuli drive neurons to fire. In visual neuroscience, this question has also been posted as characterizing…
Lift-the-flap: what, where and when for context reasoning
Mengmi Zhang, Claire Tseng, Karla Montejo +2
Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge. Both spatial and temp…
Finding any Waldo: zero-shot invariant and efficient visual search
Mengmi Zhang, Jiashi Feng, Keng Teck Ma +3
Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distr…
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Mengmi Zhang, Gabriel Kreiman
Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We…
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
William Lotter, Gabriel Kreiman, David Cox
While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neur…
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
Kevin Wu, Eric Wu, Gabriel Kreiman
Advancements in convolutional neural networks (CNNs) have made significant strides toward achieving high performance levels on multiple object recognition tasks. While some approac…
Recurrent computations for visual pattern completion
Hanlin Tang, Martin Schrimpf, Bill Lotter +6
Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occlude…
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman
Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images…
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
William Lotter, Gabriel Kreiman, David Cox
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to lea…
Unsupervised Learning of Visual Structure using Predictive Generative Networks
William Lotter, Gabriel Kreiman, David Cox
The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an unders…
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Hanlin Tang, Calin Buia, Joseph Madsen +2
Recognition of objects from partial information presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowled…
A normalization model of visual search predicts single trial human fixations in an object search task
Thomas Miconi, Laura Groomes, Gabriel Kreiman
When searching for an object in a scene, how does the brain decide where to look next? Theories of visual search suggest the existence of a global attentional map, computed by inte…
Sensitivity to Timing and Order in Human Visual Cortex
Jedediah M. Singer, Joseph R. Madsen, William S. Anderson +1
Visual recognition takes a small fraction of a second and relies on the cascade of signals along the ventral visual stream. Given the rapid path through multiple processing steps b…