32 citations · 52 across the 8 of their papers we have counts for
9 papers
LAST: Looped Audio Spectrogram Transformer
Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina +1
Increasing depth of transformer models improves recognition, but it comes at a substantial cost. Each additional layer requires more parameters, which makes the process computation…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
Orca 2: Teaching Small Language Models How to Reason
Arindam Mitra, Luciano Del Corro, Shweti Mahajan +12
Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2…
MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents
Anastasia Razdaibiedina, Alexander Brechalov
Learning semantically meaningful representations from scientific documents can facilitate academic literature search and improve performance of recommendation systems. Pre-trained…
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +4
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft pro…
Progressive Prompts: Continual Learning for Language Models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +3
We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting…
Learning multi-scale functional representations of proteins from single-cell microscopy data
Anastasia Razdaibiedina, Alexander Brechalov
Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. De…
Representation Projection Invariance Mitigates Representation Collapse
Anastasia Razdaibiedina, Ashish Khetan, Zohar Karnin +3
Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (…
Multi-defect microscopy image restoration under limited data conditions
Anastasia Razdaibiedina, Jeevaa Velayutham, Miti Modi
Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging. One of the major challenges in application of such method…