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Found 12 papers, 7 papers with code
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications.
InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data
While there is a growing literature on image-on-image regression to delineate predictive inference of an image based on multiple images, existing approaches have limitations in efficiently borrowing information between multiple imaging modalities in the prediction of an image.
Rethinking Data Distillation: Do Not Overlook Calibration
Neural networks trained on distilled data often produce over-confident output and require correction by calibration methods.
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
In particular, to quantify the uncertainties in RCA, we develop a node-level uncertainty quantification algorithm to model the overlapping support regions with high uncertainty; to handle the rarity of minority classes in miscalibration calculation, we generalize the distribution-based calibration metric to the instance level and propose the first individual calibration measurement on graphs named Expected Individual Calibration Error (EICE).
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution.
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions.
Calibrating the Rigged Lottery: Making All Tickets Reliable
Previous research has shown that deep neural networks tend to be over-confident, and we find that sparse training exacerbates this problem.
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method.
Accelerating Dataset Distillation via Model Augmentation
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones.
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property.
Estimation of COVID-19 spread curves integrating global data and borrowing information
In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries.
Applications Methodology
