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Tabular data

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Yandex Research Blog

Tabular data involves two-dimensional tables with objects (rows) and features (columns), which are used in numerous applied tasks such as classification, regression, ranking and many others.

In this post, we share exciting findings from our recent NeurIPS 2022 paper on deep learning and tabular data. We discuss how improved representations of numerical features can turn your vanilla MLPs (multilayer perceptrons) and Transformers into state-of-the-art tabular deep learning solutions that are competitive even with gradient-boosted decision trees, one of the strongest baselines in the world of tabular data problems!

Embeddings for numerical features in tabular deep learning

Two papers have been accepted for publication at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022).

Papers accepted to EMNLP 2022

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Graphs are a natural way to represent data from various domains such as social networks, molecules, text, code, etc. We develop and analyze algorithms for graph-structured data.

Nearest neighbor search is a long-standing problem arising in a large number of machine learning applications, such as recommender services, information retrieval, and others.

Nearest Neighbor Search (NNS) is a long-standing problem arising in many machine learning applications, such as recommender services, information retrieval and others. The goal of the nearest neighbor search is to find the closest element to a given query vector within a database of n elements. Since the sizes of databases in practical tasks are constantly increasing, the scalability of NNS becomes crucial.

Efficient NNS receives much attention from the machine learning community. The problem is well-solved for low-dimensional spaces. However, most practical applications need effective and accurate search in high dimensional ones. Due to the curse of dimensionality, for large d the algorithms for exact NNS provide little improvement over a brute-force algorithm. Thus, both researchers and practitioners are interested in approximate solutions which are efficient and quite accurate.

Recently, methods based on similarity graphs were shown to outperform tree-based and LSH-based techniques according to the public benchmarks. In this post, we discuss graph-based methods from different perspectives. In particular, we cover their scalability, applicability to a wide range of applications, and theoretical properties.

Graph-based nearest neighbor search

Five papers have been accepted for publication at the Conference on Neural Information Processing Systems (NeurIPS 2022).

Papers accepted to NeurIPS 2022

Generative models in computer vision are powerful tool for various applications.

Yandex Research team regularly contributes to the computer vision research community, mostly in the field of image retrieval and generative modelling.

In a series of papers, we investigated the potential of generative adversarial networks (GANs) to manipulate natural images. Our unsupervised methods exploit pretrained GANs for advanced semantic editing and object segmentation.

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Posts

Embeddings for numerical features in tabular deep learning

Papers accepted to EMNLP 2022

Graph-based nearest neighbor search

Papers accepted to NeurIPS 2022

What deep generative models can do with images

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