Search Results for author:
Found 20 papers, 12 papers with code
NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
The first approach follows the token classification schema, in which each token is assigned with a tag.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Multi-task Language Understanding
on MMLU
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs).
Ranked #1 on
Image Classification
on CUB-200-2011
Cross-Modal Conceptualization in Bottleneck Models
Concept Bottleneck Models (CBMs) assume that training examples (e. g., x-ray images) are annotated with high-level concepts (e. g., types of abnormalities), and perform classification by first predicting the concepts, followed by predicting the label relying on these concepts.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
The method has been evaluated on a large test set for English.
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
In the test set the frequency of all entity types is even.
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
With a top accuracy of $97. 61\%$ on MNIST, $97. 51\%$ on N-MNIST, and $85. 84\%$ on Fashion-MNIST, NALSM achieved comparable performance to current fully-connected multi-layer spiking neural networks trained via backpropagation.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
Inno at SemEval-2020 Task 11: Leveraging Pure Transfomer for Multi-Class Propaganda Detection
The paper presents the solution of team {''}Inno{''} to a SEMEVAL 2020 task 11 {''}Detection of propaganda techniques in news articles{''}.
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency.
Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for Multi-Class Propaganda Detection
The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11 "Detection of propaganda techniques in news articles".
So What's the Plan? Mining Strategic Planning Documents
In this paper we present a corpus of Russian strategic planning documents, RuREBus.
Realistic Physics Based Character Controller
Over the course of the last several years there was a strong interest in application of modern optimal control techniques to the field of character animation.
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations
Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
Practical Secure Aggregation for Federated Learning on User-Held Data
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves.
