Deep learning (DL) models for tabular data problems are receiving
increa...
Recent advances in diffusion models enable many powerful instruments for...
In reliable decision-making systems based on machine learning, models ha...
Node classification is a classical graph representation learning task on...
Text-to-image generation models represent the next step of evolution in ...
Denoising diffusion probabilistic models are currently becoming the lead...
Homophily is a graph property describing the tendency of edges to connec...
Recent deep learning models for tabular data currently compete with the
...
Despite the broad range of algorithms for Approximate Nearest Neighbor
S...
Recently, Transformer-like deep architectures have shown strong performa...
The literature has proposed several methods to finetune pretrained GANs ...
Denoising diffusion probabilistic models have recently received much res...
Recent advances in high-fidelity semantic image editing heavily rely on ...
Normalizing flows are a powerful class of generative models demonstratin...
The necessity of deep learning for tabular data is still an unanswered
q...
Constructing disentangled representations is known to be a difficult tas...
Recent work demonstrated the benefits of studying continuous-time dynami...
Generative Adversarial Networks (GANs) are currently an indispensable to...
Since collecting pixel-level groundtruth data is expensive, unsupervised...
These days deep neural networks are ubiquitously used in a wide range of...
The latent spaces of typical GAN models often have semantically meaningf...
In this paper, we introduce Random Path Generative Adversarial Network
(...
Similarity graphs are an active research direction for the nearest neigh...
Learning useful representations is a key ingredient to the success of mo...
Learning useful representations is a key ingredient to the success of mo...
Nowadays, deep neural networks (DNNs) have become the main instrument fo...
In plenty of machine learning applications, the most relevant items for ...
We tackle the problem of unsupervised visual descriptors compression, wh...
Recently similarity graphs became the leading paradigm for efficient nea...
In this work we introduce impostor networks, an architecture that allows...
This work addresses the problem of billion-scale nearest neighbor search...
We consider the task of lossy compression of high-dimensional vectors th...