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Found 14 papers, 5 papers with code
Using Cooperative Game Theory to Prune Neural Networks
We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks.
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation.
Explainability Techniques for Chemical Language Models
Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models.
Diffusion models with location-scale noise
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it.
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative perspective and for fundamental reasons.
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
We address the problem of safe reinforcement learning from pixel observations.
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team.
Lyapunov Exponents for Diversity in Differentiable Games
We generalize this idea to non-conservative, multi-agent gradient systems by proposing a method - denoted Generalized Ridge Rider (GRR) - for finding arbitrary bifurcation points.
Gradients are Not All You Need
Differentiable programming techniques are widely used in the community and are responsible for the machine learning renaissance of the past several decades.
Gotta Go Fast with Score-Based Generative Models
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data.
Gotta Go Fast When Generating Data with Score-Based Models
For high-resolution images, our method leads to significantly higher quality samples than all other methods tested.
Ranked #8 on
Image Generation
on CIFAR-10
(Inception score metric)
Novel Uncertainty Framework for Deep Learning Ensembles
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression.
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
Medical image classification involves thresholding of labels that represent malignancy risk levels.
Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications
The method then compares each curve to the image to rate its correspondence with the outline of the real liquid surface by examining various image properties in the area surrounding each point of the curve.
