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Found 33 papers, 4 papers with code
NLPRL System for Very Low Resource Supervised Machine Translation
This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task.
Transformer-based Neural Machine Translation System for Hindi – Marathi: WMT20 Shared Task
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.
Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 2020
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.
Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling
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.
Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery
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.
Bias in Evaluation Processes: An Optimization-Based Model
We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution.
Random Separating Hyperplane Theorem and Learning Polytopes
Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes.
Universal Weak Coreset
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.
Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding
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.
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning
Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT).
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques
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.
A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks
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.
A Regression Approach to Learning-Augmented Online Algorithms
The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms.
Online Algorithms with Multiple Predictions
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.
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
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.
Application of Hierarchical Temporal Memory Theory for Document Categorization
The current work intends to study the performance of the Hierarchical Temporal Memory(HTM) theory for automated classification of text as well as documents.
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images
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.
Algorithms for finding $k$ in $k$-means
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 ?
Integrating Acting, Planning and Learning in Hierarchical Operational Models
We present new planning and learning algorithms for RAE, the Refinement Acting Engine.
Advaita: Bug Duplicity Detection System
To improve software quality, bugs are filed using a bug tracking system.
NLPRL at WAT2019: Transformer-based Tamil -- English Indic Task Neural Machine Translation System
This paper describes the Machine Translation system for Tamil-English Indic Task organized at WAT 2019.
Landmark Detection in Low Resolution Faces with Semi-Supervised Learning
Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
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
Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment
Heatmap regression has been used for landmark localization for quite a while now.
Ranked #22 on
Face Alignment
on COFW
Intelligent Personal Assistant with Knowledge Navigation
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.
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
Different from existing approaches of modeling these relationships, we propose learnable transform functions which captures the relationships between keypoints at feature level.
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors
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
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
Head Pose Estimation of Occluded Faces using Regularized Regression
This paper presents regression methods for estimation of head pose from occluded 2-D face images.
Face Alignment by Local Deep Descriptor Regression
We also present a face alignment algorithm based on regression using these local descriptors.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
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.
