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Found 11 papers, 0 papers with code
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks.
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
Implicit neural networks have demonstrated remarkable success in various tasks.
Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models
Existing works tend to use either image-guided methods, which provide a visual reference but lack control over semantic coherence, or text-guided methods, which ensure faithfulness to text guidance but lack visual quality.
Global Convergence of Over-parameterized Deep Equilibrium Models
A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Optimization Induced Equilibrium Networks
In this paper, we investigate an emerging question: can an implicit equilibrium model's equilibrium point be regarded as the solution of an optimization problem?
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.
Spectrum concentration in deep residual learning: a free probability approach
We revisit the initialization of deep residual networks (ResNets) by introducing a novel analytical tool in free probability to the community of deep learning.
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
The location of broken insulators in aerial images is a challenging task.
Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams
Recently, there has been a growing interest in monitoring brain activity for individual recognition system.
