no code implementations • LREC (MWE) 2022 • Albina Khusainova, Vitaly Romanov, Adil Khan
Modern encoder-decoder based neural machine translation (NMT) models are normally trained on parallel sentences.
no code implementations • 26 Aug 2023 • Aditya Kasliwal, Sankarshanaa Sagaram, Laven Srivastava, Pratinav Seth, Adil Khan
This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains.
no code implementations • 12 Aug 2023 • Roman Garaev, Bader Rasheed, Adil Khan
This hypothesis suggests that training a DNN on a dataset consisting solely of robust features should produce a model resistant to adversarial attacks.
1 code implementation • 23 May 2023 • Andrey Palaev, Rustam A. Lukmanov, Adil Khan
Controlled data generation with GANs is desirable but challenging due to the nonlinearity and high dimensionality of their latent spaces.
1 code implementation • 6 Nov 2022 • Pratinav Seth, Adil Khan, Ananya Gupta, Saurabh Kumar Mishra, Akshat Bhandari
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy.
no code implementations • 13 Jul 2022 • Patrik Joslin Kenfack, Kamil Sabbagh, Adín Ramírez Rivera, Adil Khan
Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs).
no code implementations • 6 May 2022 • Gcinizwe Dlamini, Imad Eddine Ibrahim Bekkouch, Adil Khan, Leon Derczynski
This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English.
no code implementations • 4 Apr 2021 • Sobirdzhon Bobiev, Adil Khan, Syed Muhammad Ahsan Raza Kazmi
In class-incremental learning, the objective is to learn a number of classes sequentially without having access to the whole training data.
no code implementations • EACL (VarDial) 2021 • Albina Khusainova, Adil Khan, Adín Ramírez Rivera, Vitaly Romanov
The choice of parameter sharing strategy in multilingual machine translation models determines how optimally parameter space is used and hence, directly influences ultimate translation quality.
no code implementations • NeurIPS 2020 • Adil Khan, Khadija Fraz
Accordingly, the IHDA performs DA in a deep feature space, at level l, by transforming it into a distribution space and synthesizing new samples using the learned distributions for data points that lie in hard-to-classify regions, which is estimated by analyzing the neighborhood characteristics of each data point.
no code implementations • 10 Oct 2020 • Adín Ramírez Rivera, Adil Khan, Imad E. I. Bekkouch, Taimoor S. Sheikh
Anomaly detection suffers from unbalanced data since anomalies are quite rare.
1 code implementation • 27 Oct 2019 • Vladislav Kurenkov, Bulat Maksudov, Adil Khan
In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear.
no code implementations • 1 Oct 2019 • Ilshat Gibadullin, Aidar Valeev, Albina Khusainova, Adil Khan
Neural machine translation has become the state-of-the-art for language pairs with large parallel corpora.
Low-Resource Neural Machine Translation Transfer Learning +1
no code implementations • 1 Oct 2019 • Aidar Valeev, Ilshat Gibadullin, Albina Khusainova, Adil Khan
Neural machine translation is the current state-of-the-art in machine translation.
1 code implementation • 31 Mar 2019 • Albina Khusainova, Adil Khan, Adín Ramírez Rivera
We evaluate state-of-the-art word embedding models for two languages using our proposed datasets for Tatar and the original datasets for English and report our findings on performance comparison.
no code implementations • 11 Jan 2018 • Denis Usachev, Azat Khusnutdinov, Manuel Mazzara, Adil Khan, Ivan Panchenko
In this paper we develop an open source DPA and smart home system as a 3-rd party extension to show the functionality of the assistant.
Human-Computer Interaction
no code implementations • 5 Sep 2017 • Konstantin Sozykin, Stanislav Protasov, Adil Khan, Rasheed Hussain, Jooyoung Lee
Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning.