Hippocampus
51 papers with code • 0 benchmarks • 0 datasets
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Latest papers
Theta sequences as eligibility traces: a biological solution to credit assignment
Credit assignment problems, for example policy evaluation in RL, often require bootstrapping prediction errors through preceding states \textit{or} maintaining temporally extended memory traces; solutions which are unfavourable or implausible for biological networks of neurons.
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
uncbiag/naisr
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However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate.
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning.
An automated, geometry-based method for hippocampal shape and thickness analysis
deep-mi/hipsta
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In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature.
Structured Recognition for Generative Models with Explaining Away
changmin-yu/structured-recognition-neurips-2022
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A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
Continual Learning, Fast and Slow
phquang/DualNet
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Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL).
Expanding continual few-shot learning benchmarks to include recognition of specific instances
cerenaut/cfsl
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Continual learning and few-shot learning are important frontiers in progress towards broader Machine Learning (ML) capabilities.
Label-Efficient Online Continual Object Detection in Streaming Video
showlab/efficient-cls
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ICCV 2023
Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.
Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
m.reza.rezaei72/dynamical_deep_neuron
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The D4 brings deep neural networks' expressiveness and scalability to the SSM formulation letting us build a novel solution that efficiently estimates the underlying state processes through high-dimensional observation signal.
3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation
Capsule network is a recent new architecture that has achieved better robustness in part-whole representation learning by replacing pooling layers with dynamic routing and convolutional strides, which has shown potential results on popular tasks such as digit classification and object segmentation.
