no code implementations • 21 May 2016 • Gaurav Singh, Benjamin Piwowarski
We present a novel method for efficiently searching top-k neighbors for documents represented in high dimensional space of terms based on the cosine similarity.
no code implementations • 26 May 2016 • Gaurav Singh, Fabrizio Silvestri, John Shawe-Taylor
In a traditional setting, classifiers are trained to approximate a target function $f:X \rightarrow Y$ where at least a sample for each $y \in Y$ is presented to the training algorithm.
1 code implementation • 20 Jul 2016 • Sandra Mitrović, Gaurav Singh
There is an abundance of temporal and non-temporal data in banking (and other industries), but such temporal activity data can not be used directly with classical machine learning models.
no code implementations • 29 Jan 2018 • Gaurav Singh, James Thomas, John Shawe-Taylor
The first step in a systematic review task is to identify all the studies relevant to the review.
no code implementations • 30 Jul 2018 • Gaurav Singh, John Shawe-Taylor
Deep neural networks have gained tremendous popularity in last few years.
1 code implementation • EMNLP 2018 • Gaurav Singh, James Thomas, Iain J. Marshall, John Shawe-Taylor, Byron C. Wallace
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).
no code implementations • NAACL 2019 • Gaurav Singh, Parminder Bhatia
Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them.
2 code implementations • 21 Oct 2019 • Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas
The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances.
no code implementations • 26 Aug 2020 • Gaurav Singh
This paper discusses the design of the system used for providing a solution for the problem given at SemEval-2020 Task 9 where sentiment analysis of code-mixed language Hindi and English needed to be performed.
no code implementations • 6 Nov 2020 • Shreya Sarkar, Raghwendra Kumar, Gaurav Singh, Debabrata Biswas
For a compound geometry with local field enhancement by a factor of around 1000, a hybrid model is used where the vacuum field calculated using COMSOL is imported into the Particle-In-Cell code PASUPAT where the emission module is implemented.
Applied Physics Mesoscale and Nanoscale Physics Accelerator Physics Computational Physics Plasma Physics
no code implementations • 29 Jan 2021 • Shobha Sundar Ram, Gaurav Singh, Gourab Ghatak
Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section.
no code implementations • 24 Feb 2021 • Gaurav Singh
The models were created using various machine learning algorithms such as SVM, KNN, Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting classifiers.
no code implementations • 24 Aug 2021 • Gaurav Singh, Siffi Singh, Joshua Wong, Amir Saffari
To address this issue, we propose methods to artificially create some of this metadata for synthetic tables.
1 code implementation • 27 Sep 2021 • Chenglong Li, Emmeric Tanghe, Jaron Fontaine, Luc Martens, Jac Romme, Gaurav Singh, Eli de Poorter, Wout Joseph
Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization.
no code implementations • 6 Dec 2022 • Paridhi Singh, Gaurav Singh, Arun Kumar
Determining accurate bird's eye view (BEV) positions of objects and tracks in a scene is vital for various perception tasks including object interactions mapping, scenario extraction etc., however, the level of supervision required to accomplish that is extremely challenging to procure.
no code implementations • 17 Jan 2023 • Bipasha Sen, Aditya Agarwal, Gaurav Singh, Brojeshwar B., Srinath Sridhar, Madhava Krishna
Unlike existing methods that depend on an external canonicalization, SCARP performs canonicalization, pose estimation, and shape completion in a single network, improving the performance by 45% over the existing baselines.
1 code implementation • 22 May 2023 • Saurabh Srivastava, Gaurav Singh, Shou Matsumoto, Ali Raz, Paulo Costa, Joshua Poore, Ziyu Yao
In this work, we present the first dataset, MailEx, for performing event extraction from conversational email threads.
no code implementations • 8 Jun 2023 • Md Maidul Islam, Md Omar Faruque, Joshua Butterfield, Gaurav Singh, Thomas A. Cooke
Using neural networks with machine learning can aid in accurately classifying the recorded waveforms and help power system engineers diagnose and rectify the root causes of problems.
no code implementations • NeurIPS 2023 • Bipasha Sen, Gaurav Singh, Aditya Agarwal, Rohith Agaram, K Madhava Krishna, Srinath Sridhar
Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects.
no code implementations • 6 Apr 2024 • Gaurav Singh, Sanket Kalwar, Md Faizal Karim, Bipasha Sen, Nagamanikandan Govindan, Srinath Sridhar, K Madhava Krishna
Efficiently generating grasp poses tailored to specific regions of an object is vital for various robotic manipulation tasks, especially in a dual-arm setup.
1 code implementation • COLING 2022 • Siffi Singh, Alham Fikri Aji, Gaurav Singh, Christos Christodoulopoulos
Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set.