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Found 19 papers, 2 papers with code
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains.
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function.
Cliff-Learning
We conduct an in-depth investigation of foundation-model cliff-learning and study toy models of the phenomenon.
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories.
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
In this work, we show how data labeled with semantically continuous attributes can be utilized to conduct a quantitative evaluation of latent-space interpolation algorithms, for variational autoencoders.
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Brain-wide measurements of activity and anatomical connectivity of the $\textit{C. elegans}$ nervous system in principle allow for the development of detailed mechanistic computational models.
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
To address this issue, we introduce a new challenging task to generate HD maps.
Learning Guided Electron Microscopy with Active Acquisition
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.
On the Predictability of Pruning Across Scales
We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task.
L3 Fusion: Fast Transformed Convolutions on CPUs
Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of required operations.
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
A Constructive Prediction of the Generalization Error Across Scales
In this work, we present a functional form which approximates well the generalization error in practice.
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching.
Deep Learning is Robust to Massive Label Noise
Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks.
Morphological Error Detection in 3D Segmentations
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology.
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed.
Toward Streaming Synapse Detection with Compositional ConvNets
Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images.
A Multi-Pass Approach to Large-Scale Connectomics
The field of connectomics faces unprecedented "big data" challenges.
Deep Tensor Convolution on Multicores
Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features.
