1 code implementation • ECCV 2020 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process.
1 code implementation • 1 Jun 2023 • Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, FengTing Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia
We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring.
1 code implementation • 31 Oct 2023 • Guoxuan Xia, Duolikun Danier, Ayan Das, Stathi Fotiadis, Farhang Nabiei, Ushnish Sengupta, Alberto Bernacchia
As a simple fix, we propose to instead reparameterise the score (at inference) by dividing it by the average absolute value of previous score estimates at that time step collected from offline high NFE generations.
no code implementations • 1 Aug 2017 • Partha Pratim Roy, Ayan Kumar Bhunia, Ayan Das, Prasenjit Dey, Umapada Pal
To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way.
no code implementations • 30 Sep 2015 • Ayan Das, Sourangshu Bhattacharya
Experimental results on a variety of toy and real world datasets show that our approach is significantly more accurate than parameter averaging for high number of partitions.
no code implementations • CONLL 2017 • Ayan Das, Affan Zaffar, Sudeshna Sarkar
This paper describes our dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
no code implementations • WS 2016 • Ayan Das, Agnivo Saha, Sudeshna Sarkar
A parser is trained and applied to the Hindi sentences of the parallel corpus and the parse trees are projected to construct probable parse trees of the corresponding Bengali sentences.
no code implementations • WS 2016 • Ayan Das, Pranay Yerra, Ken Kumar, Sudeshna Sarkar
Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT).
no code implementations • 27 Feb 2020 • Ayan Das, Sudeshna Sarkar
We present a shallow parser guided cross-lingual model transfer approach in order to address the syntactic differences between source and target languages more effectively.
1 code implementation • CVPR 2021 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations.
no code implementations • 29 Mar 2021 • Sabiha Majumder, Ayan Das, Appilineni Kushal, Sumithra Sankaran, Vishwesha Guttal
In this paper, we show that demographic noise may, in fact, promote abrupt transitions in systems that would otherwise show continuous transitions.
no code implementations • ICLR 2022 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression.
no code implementations • ICON 2019 • Ayan Das, Sudeshna Sarkar
We present an approach for cross-lingual transfer of dependency parser so that the parser trained on a single source language can more effectively cater to diverse target languages.
no code implementations • ICON 2020 • Alapan Kuila, Ayan Das, Sudeshna Sarkar
This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020.
no code implementations • 7 Apr 2023 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality).
no code implementations • 14 Oct 2023 • Tripti Kumari, Chakali Sai Charan, Ayan Das
This paper presents the system submitted by the team from IIT(ISM) Dhanbad in FIRE IRSE 2023 shared task 1 on the automatic usefulness prediction of code-comment pairs as well as the impact of Large Language Model(LLM) generated data on original base data towards an associated source code.
no code implementations • 7 Dec 2023 • Hmrishav Bandyopadhyay, Subhadeep Koley, Ayan Das, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Ayan Kumar Bhunia, Yi-Zhe Song
In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills.