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Found 10 papers, 3 papers with code
Addressing Sample Inefficiency in Multi-View Representation Learning
In this work, we provide theoretical insights on the implicit bias of the BarlowTwins and VICReg loss that can explain these heuristics and guide the development of more principled recommendations.
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely.
Investigating Power laws in Deep Representation Learning
Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with self-supervised learning (SSL).
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks.
Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis
Neuroimaging data analysis often involves \emph{a-priori} selection of data features to study the underlying neural activity.
SIMILARnet: Simultaneous Intelligent Localization and Recognition Network
Global Average Pooling (GAP) [4] has been used previously to generate class activation for image classification tasks.
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues.
Handwriting Profiling using Generative Adversarial Networks
Handwriting is a skill learned by humans from a very early age.
DeepVO: A Deep Learning approach for Monocular Visual Odometry
Deep Learning based techniques have been adopted with precision to solve a lot of standard computer vision problems, some of which are image classification, object detection and segmentation.
