research
∙
07/26/2023
TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning
Deep learning (DL) models for tabular data problems are receiving increa...
share
research
∙
04/10/2023
Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
Recent advances in diffusion models enable many powerful instruments for...
research
∙
02/27/2023
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
In reliable decision-making systems based on machine learning, models ha...
research
∙
02/22/2023
A critical look at the evaluation of GNNs under heterophily: are we really making progress?
Node classification is a classical graph representation learning task on...
research
∙
02/09/2023
Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation
Text-to-image generation models represent the next step of evolution in ...
research
∙
09/30/2022
TabDDPM: Modelling Tabular Data with Diffusion Models
Denoising diffusion probabilistic models are currently becoming the lead...
research
∙
09/13/2022
Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy
Homophily is a graph property describing the tendency of edges to connec...
research
∙
07/07/2022
Revisiting Pretraining Objectives for Tabular Deep Learning
Recent deep learning models for tabular data currently compete with the ...
research
∙
05/08/2022
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Despite the broad range of algorithms for Approximate Nearest Neighbor S...
research
∙
03/10/2022
On Embeddings for Numerical Features in Tabular Deep Learning
Recently, Transformer-like deep architectures have shown strong performa...
research
∙
02/17/2022
When, Why, and Which Pretrained GANs Are Useful?
The literature has proposed several methods to finetune pretrained GANs ...
research
∙
12/06/2021
Label-Efficient Semantic Segmentation with Diffusion Models
Denoising diffusion probabilistic models have recently received much res...
research
∙
11/29/2021
Latent Transformations via NeuralODEs for GAN-based Image Editing
Recent advances in high-fidelity semantic image editing heavily rely on ...
research
∙
06/24/2021
Distilling the Knowledge from Normalizing Flows
Normalizing flows are a powerful class of generative models demonstratin...
research
∙
06/22/2021
Revisiting Deep Learning Models for Tabular Data
The necessity of deep learning for tabular data is still an unanswered q...
research
∙
02/11/2021
Disentangled Representations from Non-Disentangled Models
Constructing disentangled representations is known to be a difficult tas...
research
∙
02/08/2021
Functional Space Analysis of Local GAN Convergence
Recent work demonstrated the benefits of studying continuous-time dynami...
research
∙
11/27/2020
Navigating the GAN Parameter Space for Semantic Image Editing
Generative Adversarial Networks (GANs) are currently an indispensable to...
research
∙
06/08/2020
Big GANs Are Watching You: Towards Unsupervised Object Segmentation with Off-the-Shelf Generative Models
Since collecting pixel-level groundtruth data is expensive, unsupervised...
research
∙
04/01/2020
Editable Neural Networks
These days deep neural networks are ubiquitously used in a wide range of...
research
∙
02/10/2020
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
The latent spaces of typical GAN models often have semantically meaningf...
research
∙
12/23/2019
RPGAN: GANs Interpretability via Random Routing
In this paper, we introduce Random Path Generative Adversarial Network (...
research
∙
11/27/2019
Towards Similarity Graphs Constructed by Deep Reinforcement Learning
Similarity graphs are an active research direction for the nearest neigh...
research
∙
10/08/2019
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Learning useful representations is a key ingredient to the success of mo...
research
∙
10/08/2019
Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs
Learning useful representations is a key ingredient to the success of mo...
research
∙
09/13/2019
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Nowadays, deep neural networks (DNNs) have become the main instrument fo...
research
∙
08/19/2019
Relevance Proximity Graphs for Fast Relevance Retrieval
In plenty of machine learning applications, the most relevant items for ...
research
∙
08/11/2019
Unsupervised Neural Quantization for Compressed-Domain Similarity Search
We tackle the problem of unsupervised visual descriptors compression, wh...
research
∙
05/27/2019
Learning to Route in Similarity Graphs
Recently similarity graphs became the leading paradigm for efficient nea...
research
∙
06/13/2018
Impostor Networks for Fast Fine-Grained Recognition
In this work we introduce impostor networks, an architecture that allows...
research
∙
02/07/2018
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
This work addresses the problem of billion-scale nearest neighbor search...
research
∙
06/05/2016
