no code implementations • WMT (EMNLP) 2020 • Rupjyoti Baruah, Rajesh Kumar Mundotiya, Amit Kumar, Anil Kumar Singh
This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task.
1 code implementation • Findings (EMNLP) 2021 • Ayan Sengupta, Amit Kumar, Sourabh Kumar Bhattacharjee, Suman Roy
Experimental results show that our gated architecture with pre-trained language models perform significantly better that the non-gated counterparts and other state-of-the-art error correction models in correcting spelling and grammatical errors.
no code implementations • WMT (EMNLP) 2020 • Amit Kumar, Rupjyoti Baruah, Rajesh Kumar Mundotiya, Anil Kumar Singh
This paper reports the results for the Machine Translation (MT) system submitted by the NLPRL team for the Hindi – Marathi Similar Translation Task at WMT 2020.
no code implementations • loresmt (AACL) 2020 • Amit Kumar, Rajesh Kumar Mundotiya, Anil Kumar Singh
This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri–Hindi and Magahi–Hindi language pairs at LoResMT 2020 shared task.
1 code implementation • 18 Dec 2023 • Daman Deep Singh, Amit Kumar, Abhijnan Chakraborty
In this paper, we introduce a realistic generalization of k-SERVER without such assumptions - the k-FOOD problem, where requests with source-destination locations and an associated pickup time window arrive in an online fashion, and each has to be served by exactly one of the available k servers.
1 code implementation • NeurIPS 2023 • L. Elisa Celis, Amit Kumar, Anay Mehrotra, Nisheeth K. Vishnoi
We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution.
no code implementations • 21 Jul 2023 • Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar
Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes.
no code implementations • 26 May 2023 • Ragesh Jaiswal, Amit Kumar
Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers.
no code implementations • 21 May 2023 • Amit Kumar, Shantipriya Parida, Ajay Pratap, Anil Kumar Singh
One reason for this is the relative morphological richness of Indian languages, while another is that most of them fall into the extremely low resource or zero-shot categories.
no code implementations • 31 Mar 2023 • Amit Kumar, Ajay Pratap, Anil Kumar Singh
Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT).
no code implementations • 3 Mar 2023 • Amit Kumar, Rupjyoti Baruah, Ajay Pratap, Mayank Swarnkar, Anil Kumar Singh
If the evaluation is as rigorous as resource-rich languages, both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) can produce good results with such large amounts of data.
no code implementations • 4 Jan 2023 • Rebekah Aduddell, James Fairbanks, Amit Kumar, Pablo S. Ocal, Evan Patterson, Brandon T. Shapiro
Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems.
no code implementations • NeurIPS 2021 • Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms.
no code implementations • 8 May 2022 • Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
In this paper, we give a generic algorithmic framework for online covering problems with multiple predictions that obtains an online solution that is competitive against the performance of the best predictor.
no code implementations • 28 Mar 2022 • Xiaoyu Xiang, Jon Morton, Fitsum A Reda, Lucas Young, Federico Perazzi, Rakesh Ranjan, Amit Kumar, Andrea Colaco, Jan Allebach
Compared with previous methods, our network can effectively handle the misalignment between the input and the reference without requiring facial priors and learn the aggregated reference set representation in an end-to-end manner.
no code implementations • 29 Dec 2021 • Deven Shah, Pinak Ghate, Manali Paranjape, Amit Kumar
The current work intends to study the performance of the Hierarchical Temporal Memory(HTM) theory for automated classification of text as well as documents.
no code implementations • CVPR 2022 • Prithviraj Dhar, Amit Kumar, Kirsten Kaplan, Khushi Gupta, Rakesh Ranjan, Rama Chellappa
To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting.
no code implementations • 8 Dec 2020 • Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar
Two challenges are open: (i) Is there a data-determined definition of $k$ which is provably correct and (ii) Is there a polynomial time algorithm to find $k$ from data ?
no code implementations • 3 Dec 2020 • Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra
Video transmission applications (e. g., conferencing) are gaining momentum, especially in times of global health pandemic.
no code implementations • 9 Mar 2020 • Sunandita Patra, James Mason, Amit Kumar, Malik Ghallab, Paolo Traverso, Dana Nau
We present new planning and learning algorithms for RAE, the Refinement Acting Engine.
no code implementations • 24 Jan 2020 • Amit Kumar, Manohar Madanu, Hari Prakash, Lalitha Jonnavithula, Srinivasa Rao Aravilli
To improve software quality, bugs are filed using a bug tracking system.
no code implementations • WS 2019 • Amit Kumar, Anil Kumar Singh
This paper describes the Machine Translation system for Tamil-English Indic Task organized at WAT 2019.
no code implementations • 30 Jul 2019 • Amit Kumar, Rama Chellappa
Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images.
1 code implementation • ICCV 2019 • Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation Vehicle Re-Identification
no code implementations • CVPR 2018 • Amit Kumar, Rama Chellappa
Heatmap regression has been used for landmark localization for quite a while now.
Ranked #22 on Face Alignment on COFW
no code implementations • 28 Apr 2017 • Amit Kumar, Rahul Dutta, Harbhajan Rai
An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content.
no code implementations • 6 Apr 2017 • Amit Kumar, Rama Chellappa
Different from existing approaches of modeling these relationships, we propose learnable transform functions which captures the relationships between keypoints at feature level.
no code implementations • 16 Feb 2017 • Amit Kumar, Azadeh Alavi, Rama Chellappa
In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.
Ranked #16 on Head Pose Estimation on BIWI
no code implementations • SEMEVAL 2016 • Ayush Kumar, Sarah Kohail, Amit Kumar, Asif Ekbal, Chris Biemann
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • 9 May 2016 • Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
no code implementations • 2 Feb 2016 • Amit Kumar, Rishabh Bindal, Soumya Indela, Michael Rotkowitz
This paper presents regression methods for estimation of head pose from occluded 2-D face images.
no code implementations • 29 Jan 2016 • Amit Kumar, Rajeev Ranjan, Vishal Patel, Rama Chellappa
We also present a face alignment algorithm based on regression using these local descriptors.
no code implementations • 28 Jan 2016 • Rama Chellappa, Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Vishal M. Patel, Carlos D. Castillo
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.