Search Results for author:
Found 23 papers, 4 papers with code
Self-Supervised Learning of Color Constancy
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions.
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
For this we propose a computational model of visual representation learning during dyadic play.
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Human intelligence and human consciousness emerge gradually during the process of cognitive development.
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning
In recent studies, augmentation-based contrastive learning methods have been proposed for learning representations that are invariant or equivariant to pre-defined data augmentation operations.
Sequence and Circle: Exploring the Relationship Between Patches
The CRE considers the central patch as the center of the circle and measures the distance of the remaining patches from the center based on the four neighborhoods principle.
Time to augment self-supervised visual representation learning
Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation.
Degeneracy in epilepsy: Multiple Routes to Hyperexcitable Brain Circuits and their Repair
Third, at the system level, we provide examples for degeneracy in the intricate interactions between the immune and nervous system.
Embodied vision for learning object representations
During each play session the agent views an object in multiple orientations before turning its body to view another object.
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data
Specifically, we aggregate multiple spectra from the same patient into a "bag" for classification and apply data augmentation techniques.
Contrastive Learning Through Time
We propose a family of CLTT algorithms based on state-of-the-art contrastive learning methods and test them on three data sets.
Recurrent Feedback Improves Recognition of Partially Occluded Objects
Recurrent connectivity in the visual cortex is believed to aid object recognition for challenging conditions such as occlusion.
Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
The high resolution fovea can drive precise short range movements.
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior.
Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders
We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements.
REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems
The first contribution of this work is to study the challenges posed by the previously proposed benchmark `REAL competition' aiming to foster the development of truly open-ended learning robot architectures.
Staging Epileptogenesis with Deep Neural Networks
Specifically, continuous intracranial EEG recordings were collected from a rodent model where epilepsy is induced by electrical perforant pathway stimulation (PPS).
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems.
Towards Early Diagnosis of Epilepsy from EEG Data
This demonstrates the feasibility of EPG prediction from EEG recordings.
Recurrent Connectivity Aids Recognition of Partly Occluded Objects
Overall, our results suggest that both artificial and biological neural networks can exploit recurrence for improved object recognition.
Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders
Recurrent connections in the visual cortex are thought to aid object recognition when part of the stimulus is occluded.
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
We conclude that modern deep learning approaches can reach state-of-the-art performance on epileptic EEG classification and automated seizure onset zone identification tasks when trained on raw EEG data.
An active efficient coding model of the optokinetic nystagmus
This framework models the joint emergence of both perception and behavior, and accounts for the importance of the development of normal vergence control and binocular vision in achieving normal monocular OKN (mOKN) behaviors.
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
We extend the framework of efficient coding, which has been used to model the development of sensory processing in isolation, to model the development of the perception/action cycle.
