3 citations · 3 across the 8 of their papers we have counts for
8 papers
Progressive Cramming: Reliable Token Compression and What It Reveals
Dmitrii Tarasov, Timofei Lashukov, Elizaveta Goncharova +1
Token cramming compresses sequences into learned embeddings with near-perfect reconstruction, but fixed token budgets and 99\% accuracy thresholds leave it unclear whether residual…
Simple Vision-Language Math Reasoning via Rendered Text
Matvey Skripkin, Elizaveta Goncharova, Andrey Kuznetsov
We present a lightweight yet effective pipeline for training vision-language models to solve math problems by rendering LaTeX encoded equations into images and pairing them with st…
Sentence-Anchored Gist Compression for Long-Context LLMs
Dmitrii Tarasov, Elizaveta Goncharova, Kuznetsov Andrey
This work investigates context compression for Large Language Models (LLMs) using learned compression tokens to reduce the memory and computational demands of processing long seque…
Image Reconstruction as a Tool for Feature Analysis
Eduard Allakhverdov, Dmitrii Tarasov, Elizaveta Goncharova +1
Vision encoders are increasingly used in modern applications, from vision-only models to multimodal systems such as vision-language models. Despite their remarkable success, it rem…
When Less is Enough: Adaptive Token Reduction for Efficient Image Representation
Eduard Allakhverdov, Elizaveta Goncharova, Andrey Kuznetsov
Vision encoders typically generate a large number of visual tokens, providing information-rich representations but significantly increasing computational demands. This raises the q…
MOVE: A Mixture-of-Vision-Encoders Approach for Domain-Focused Vision-Language Processing
Matvey Skripkin, Elizaveta Goncharova, Dmitrii Tarasov +1
Multimodal language models (MLMs) integrate visual and textual information by coupling a vision encoder with a large language model through the specific adapter. While existing app…
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
Viktoriia Chekalina, Anton Razzhigaev, Elizaveta Goncharova +1
In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves…
Revising deep learning methods in parking lot occupancy detection
Anastasia Martynova, Mikhail Kuznetsov, Vadim Porvatov +4
Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing dri…