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Found 15 papers, 3 papers with code
Exploring Precision and Recall to assess the quality and diversity of LLMs
This paper introduces a novel evaluation framework for Large Language Models (LLMs) such as Llama-2 and Mistral, focusing on the adaptation of Precision and Recall metrics from image generation to text generation.
Optimal Budgeted Rejection Sampling for Generative Models
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models.
Ranked #14 on
Image Generation
on CelebA 64x64
Adversarial attacks for mixtures of classifiers
However, it has been shown that existing attacks are not well suited for this kind of classifiers.
Training Normalizing Flows with the Precision-Recall Divergence
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric.
On the expressivity of bi-Lipschitz normalizing flows
An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants.
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to protect neural networks against $\ell_\infty$ adversarial examples offer poor performance against $\ell_2$ adversarial examples and vice versa.
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks.
The Expressive Power of Deep Neural Networks with Circulant Matrices
Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of compact deep neural network architectures that perform well in practice.
Robust Neural Networks using Randomized Adversarial Training
This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular $\ell_\infty$ and $\ell_2$ bounded adversarial examples).
Understanding and Training Deep Diagonal Circulant Neural Networks
In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones.
Training compact deep learning models for video classification using circulant matrices
In real world scenarios, model accuracy is hardly the only factor to consider.
Recognizing Art Style Automatically in painting with deep learning
The artistic style (or artistic movement) of a painting is a rich descriptor that captures both visual and historical information about the painting.
Ranked #1 on
Artistic style classification
on RASTA
Expert Opinion Extraction from a Biomedical Database
In this paper, we tackle the problem of extracting frequent opinions from uncertain databases.
A SAT model to mine flexible sequences in transactional datasets
Traditional pattern mining algorithms generally suffer from a lack of flexibility.
Constraint-based sequence mining using constraint programming
We investigate the use of constraint programming as general framework for this task.
