
Distilling the Knowledge from Normalizing Flows
Normalizing flows are a powerful class of generative models demonstratin...
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Revisiting Deep Learning Models for Tabular Data
The necessity of deep learning for tabular data is still an unanswered q...
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Disentangled Representations from NonDisentangled Models
Constructing disentangled representations is known to be a difficult tas...
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Functional Space Analysis of Local GAN Convergence
Recent work demonstrated the benefits of studying continuoustime dynami...
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Navigating the GAN Parameter Space for Semantic Image Editing
Generative Adversarial Networks (GANs) are currently an indispensable to...
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Big GANs Are Watching You: Towards Unsupervised Object Segmentation with OfftheShelf Generative Models
Since collecting pixellevel groundtruth data is expensive, unsupervised...
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Editable Neural Networks
These days deep neural networks are ubiquitously used in a wide range of...
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Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
The latent spaces of typical GAN models often have semantically meaningf...
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RPGAN: GANs Interpretability via Random Routing
In this paper, we introduce Random Path Generative Adversarial Network (...
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Towards Similarity Graphs Constructed by Deep Reinforcement Learning
Similarity graphs are an active research direction for the nearest neigh...
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Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Learning useful representations is a key ingredient to the success of mo...
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Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs
Learning useful representations is a key ingredient to the success of mo...
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Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Nowadays, deep neural networks (DNNs) have become the main instrument fo...
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Relevance Proximity Graphs for Fast Relevance Retrieval
In plenty of machine learning applications, the most relevant items for ...
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Unsupervised Neural Quantization for CompressedDomain Similarity Search
We tackle the problem of unsupervised visual descriptors compression, wh...
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Learning to Route in Similarity Graphs
Recently similarity graphs became the leading paradigm for efficient nea...
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Impostor Networks for Fast FineGrained Recognition
In this work we introduce impostor networks, an architecture that allows...
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Revisiting the Inverted Indices for BillionScale Approximate Nearest Neighbors
This work addresses the problem of billionscale nearest neighbor search...
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Pairwise Quantization
We consider the task of lossy compression of highdimensional vectors th...
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Artem Babenko
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