Continual learning is a problem for artificial neural networks that thei...
We create a reusable Transformer, BrainBERT, for intracranial recordings...
"Forward-only" algorithms, which train neural networks while avoiding a
...
Our education system comprises a series of curricula. For example, when ...
Visual search is a ubiquitous challenge in natural vision, including dai...
As AI algorithms increasingly participate in daily activities that used ...
A tiny object in the sky cannot be an elephant. Context reasoning is cri...
Adversarial examples against AI systems pose both risks via malicious at...
While several methodologies have been proposed for the daunting task of
...
Supervised learning in artificial neural networks typically relies on
ba...
In recent years, multi-modal transformers have shown significant progres...
It is well understood that modern deep networks are vulnerable to advers...
Visual search is a ubiquitous and often challenging daily task, exemplif...
In human vision objects and their parts can be visually recognized from
...
Context is of fundamental importance to both human and machine vision – ...
Stream learning refers to the ability to acquire and transfer knowledge
...
Saccadic eye movements allow animals to bring different parts of an imag...
Deep artificial neural networks have been proposed as a model of primate...
Can we infer intentions from a person's actions? As an example problem, ...
Deep Learning has driven recent and exciting progress in computer vision...
Deep neural networks (DNNs) perform well on a variety of tasks despite t...
Context plays an important role in visual recognition. Recent studies ha...
A fundamental question for understanding brain function is what types of...
Children benefit from lift-the-flap books by taking on an active role in...
Can we infer intentions and goals from a person's actions? As an example...
Searching for a target object in a cluttered scene constitutes a fundame...
While deep neural networks take loose inspiration from neuroscience, it ...
Advancements in convolutional neural networks (CNNs) have made significa...
Making inferences from partial information constitutes a critical aspect...
Deep convolutional neural networks are generally regarded as robust func...
While great strides have been made in using deep learning algorithms to ...
The ability to predict future states of the environment is a central pil...