Search Results for author:
Found 36 papers, 15 papers with code
RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
Against Opacity: Explainable AI and Large Language Models for Effective Digital Advertising
Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising.
Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and second, it often makes mistakes in whether to use a tool at all.
Robust AI-Generated Text Detection by Restricted Embeddings
Growing amount and quality of AI-generated texts makes detecting such content more difficult.
Neural Click Models for Recommender Systems
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.
Improving Interpretability and Robustness for the Detection of AI-Generated Images
In this work, we focus on the robustness of AI-generated image (AIGI) detectors.
$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science.
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering.
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data.
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
AI-generated text boundary detection with RoFT
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated.
Ranked #2 on
Boundary Detection
on RoFT-chatgpt
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.
Machine Learning for SAT: Restricted Heuristics and New Graph Representations
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets
We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow.
Ranked #1 on
Code Generation
on CoNaLa
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society.
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search
We consider the well-known and important tasks of clone detection and information retrieval for source code.
Searching by Code: a New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets
Code search is an important and well-studied task, but it usually means searching for code by a text query.
STIR: Siamese Transformer for Image Retrieval Postprocessing
Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well.
Ranked #2 on
Metric Learning
on In-Shop
Topological Data Analysis for Speech Processing
We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT.
Personality-Driven Social Multimedia Content Recommendation
In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale.
DetIE: Multilingual Open Information Extraction Inspired by Object Detection
Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.
Ranked #1 on
Open Information Extraction
on LSOIE
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
Understanding image advertisements is a challenging task, often requiring non-literal interpretation.
Towards General Purpose Geometry-Preserving Single-View Depth Estimation
Our work shows that a model trained on this data along with conventional datasets can gain accuracy while predicting correct scene geometry.
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
High-Resolution Daytime Translation Without Domain Labels
We present the high-resolution daytime translation (HiDT) model for this task.
CommentsRadar: Dive into Unique Data on All Comments on the Web
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.
Free-Lunch Saliency via Attention in Atari Agents
We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i. e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents.
AspeRa: Aspect-Based Rating Prediction Based on User Reviews
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
Large-Scale Transfer Learning for Natural Language Generation
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.
Breast Tumor Cellularity Assessment using Deep Neural Networks
Breast cancer is one of the main causes of death worldwide.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Generative models are becoming a tool of choice for exploring the molecular space.
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
