Ashwin Srinivasan

ORCID

Publication Activity (10 Years)

Years Active: 1992-2024
Publications (10 Years): 58

Top Venues

Publications

Shreyas Bhat Brahmavar, Ashwin Srinivasan, Tirtharaj Dash, Sowmya Ramaswamy Krishnan, Lovekesh Vig, Arijit Roy, Raviprasad Aduri
Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback. AAAI (2024)
Ashwin Srinivasan, A. Baskar, Tirtharaj Dash, Devanshu Shah
Composition of relational features with an application to explaining black-box predictors. Mach. Learn. 113 (3) (2024)
Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification. EMBC (2023)
Shreyas Bhat Brahmavar, Rohit Rajesh, Tirtharaj Dash, Lovekesh Vig, Tanmay Tulsidas Verlekar, Md Mahmudul Hasan, Tariq Mahmood Khan, Erik Meijering, Ashwin Srinivasan
IKD+: Reliable Low Complexity Deep Models for Retinopathy Classification. ICIP (2023)
S. I Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig
Neuro-symbolic Meta Reinforcement Learning for Trading. CoRR (2023)
Shrey Pandit, Gautam Shroff, Ashwin Srinivasan, Lovekesh Vig
Can LLMs solve generative visual analogies? IARML@IJCAI (2023)
Shreyas Bhat Brahmavar, Rohit Rajesh, Tirtharaj Dash, Lovekesh Vig, Tanmay Tulsidas Verlekar, Md Mahmudul Hasan, Tariq M. Khan, Erik Meijering, Ashwin Srinivasan
IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification. CoRR (2023)
Ashwin Srinivasan, Michael Bain, A. Baskar, Enrico W. Coiera
A Protocol for Intelligible Interaction Between Agents That Learn and Explain. CoRR (2023)
Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan
Domain-Specific Pretraining Improves Confidence in Whole Slide Image Classification. CoRR (2023)
Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces. CoRR (2022)
Ashwin Srinivasan, A. Baskar, Tirtharaj Dash, Devanshu Shah
Composition of Relational Features with an Application to Explaining Black-Box Predictors. CoRR (2022)
Ashwin Srinivasan, Michael Bain, Enrico W. Coiera
One-way Explainability Isn't The Message. CoRR (2022)
Vedant Shah, Aditya Agrawal, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Tanmay T. Verlekar
Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss. CoRR (2022)
Aditya Challa, Ashwin Srinivasan, Michael Bain, Gautam Shroff
A Program-Synthesis Challenge for ARC-Like Tasks. ILP (2022)
Ashwin Srinivasan, Michael Bain, A. Baskar
Learning explanations for biological feedback with delays using an event calculus. Mach. Learn. 111 (7) (2022)
Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces. NeSy (2022)
Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash
Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract). AAAI (2022)
Tirtharaj Dash, Ashwin Srinivasan, A. Baskar
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment. Mach. Learn. 111 (2) (2022)
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
How to Tell Deep Neural Networks What We Know. CoRR (2021)
Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash
Solving Visual Analogies Using Neural Algorithmic Reasoning. CoRR (2021)
Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Arijit Roy
Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design. ILP (2021)
Atharv Sonwane, Sharad Chitlangia, Tirtharaj Dash, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems. CoRR (2021)
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
Incorporating Domain Knowledge into Deep Neural Networks. CoRR (2021)
Tirtharaj Dash, Ashwin Srinivasan, A. Baskar
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment. CoRR (2021)
Het Shah, Ashwin Vaswani, Tirtharaj Dash, Ramya Hebbalaguppe, Ashwin Srinivasan
Empirical Study of Data-Free Iterative Knowledge Distillation. ICANN (3) (2021)
Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig
Incorporating symbolic domain knowledge into graph neural networks. Mach. Learn. 110 (7) (2021)
Sanket Rajan Gupte, Dharm Skandh Jain, Ashwin Srinivasan, Raviprasad Aduri
MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences. BIBM (2020)
Sharan Yalburgi, Tirtharaj Dash, Ramya Hebbalaguppe, Srinidhi Hegde, Ashwin Srinivasan
An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression. ESANN (2020)
Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff
Constructing generative logical models for optimisation problems using domain knowledge. Mach. Learn. 109 (7) (2020)
Mouli Rastogi, Syed Afshan Ali, Mrinal Rawat, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan
Information Extraction from Document Images via FCA based Template Detection and Knowledge Graph Rule Induction. CVPR Workshops (2020)
Rishab Khincha, Soundarya Krishnan, Krishnan Guru-Murthy, Tirtharaj Dash, Lovekesh Vig, Ashwin Srinivasan
Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays. CoRR (2020)
Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig
Incorporating Symbolic Domain Knowledge into Graph Neural Networks. CoRR (2020)
Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan
A Case Study of Transfer of Lesion-Knowledge. iMIMIC/MIL3ID/LABELS@MICCAI (2020)
Kushagra Mahajan, Monika Sharma, Lovekesh Vig, Rishab Khincha, Soundarya Krishnan, Adithya Niranjan, Tirtharaj Dash, Ashwin Srinivasan, Gautam Shroff
CovidDiagnosis: Deep Diagnosis of COVID-19 Patients Using Chest X-Rays. TIA@MICCAI (2020)
Richa Verma, Sarmimala Saikia, Harshad Khadilkar, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan
A Reinforcement Learning Framework for Container Selection and Ship Load Sequencing in Ports. AAMAS (2019)
Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis. NeSy@IJCAI (2019)
Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis. CoRR (2019)
Ashwin Srinivasan, Lovekesh Vig, Michael Bain
Logical Explanations for Deep Relational Machines Using Relevance Information. J. Mach. Learn. Res. 20 (2019)
Tirtharaj Dash, Ashwin Srinivasan, Ramprasad S. Joshi, A. Baskar
Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines. ICANN (2) (2019)
Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Oghenejokpeme I. Orhobor, Ross D. King
Large-Scale Assessment of Deep Relational Machines. ILP (2018)
Ashwin Srinivasan, Lovekesh Vig, Michael Bain
Logical Explanations for Deep Relational Machines Using Relevance Information. CoRR (2018)
Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan
Evolutionary RL for Container Loading. CoRR (2018)
Haimonti Dutta, Ashwin Srinivasan
Consensus-based modeling using distributed feature construction with ILP. Mach. Learn. 107 (5) (2018)
Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan
Deep Reader: Information extraction from Document images via relation extraction and Natural Language. CoRR (2018)
Michael Bain, Ashwin Srinivasan
Identification of biological transition systems using meta-interpreted logic programs. Mach. Learn. 107 (7) (2018)
Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan
Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Language. ACCV Workshops (2018)
Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan
Evolutionary RL for Container Loading. ESANN (2018)
Ashwin Srinivasan, Michael Bain
An empirical study of on-line models for relational data streams. Mach. Learn. 106 (2) (2017)
Lovekesh Vig, Ashwin Srinivasan, Michael Bain, Ankit Verma
An Investigation into the Role of Domain-Knowledge on the Use of Embeddings. ILP (2017)
Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan
Hybrid BiLSTM-Siamese network for FAQ Assistance. CIKM (2017)
Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Puneet Agarwal
Generation of Near-Optimal Solutions Using ILP-Guided Sampling. CoRR (2016)
Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia
Generation of Near-Optimal Solutions Using ILP-Guided Sampling. ILP (2016)
Deepika Vatsa, Sumeet Agarwal, Ashwin Srinivasan
Learning transition models of biological regulatory and signaling networks from noisy data. CODS (2016)
Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs. CoRR (2016)
Rama Kaalia, Ashwin Srinivasan, Amit Kumar, Indira Ghosh
ILP-assisted de novo drug design. Mach. Learn. 103 (3) (2016)
Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat
Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs. CoCo@NIPS (2016)
Ashwin Srinivasan, Michael Bain, Deepika Vatsa, Sumeet Agarwal
Identification of Transition Models of Biological Systems in the Presence of Transition Noise. ILP (2015)
Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan
Succinctly summarizing machine usage via multi-subspace clustering of multi-sensor data. DSAA (2015)
Geetika Sharma, Gautam Shroff, Aditeya Pandey, Puneet Agarwal, Ashwin Srinivasan
Interactively Visualizing Summaries of Rules and Exceptions. EuroVA@EuroVis (2014)
Haimonti Dutta, Ashwin Srinivasan
Consensus-Based Modelling using Distributed Feature Construction. CoRR (2014)
Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan, Aditeya Pandey, Gaurangi Anand
Exploratory Data Analysis Using Alternating Covers of Rules and Exceptions. COMAD (2014)
Ashwin Srinivasan, Tanveer A. Faruquie, Sachindra Joshi
Data and task parallelism in ILP using MapReduce. Mach. Learn. 86 (1) (2012)
Amrita Saha, Ashwin Srinivasan, Ganesh Ramakrishnan
What Kinds of Relational Features Are Useful for Statistical Learning? ILP (2012)
Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan
ILP turns 20 - Biography and future challenges. Mach. Learn. 86 (1) (2012)
Tanveer A. Faruquie, Ashwin Srinivasan, Ross D. King
Topic Models with Relational Features for Drug Design. ILP (2012)
Ashwin Srinivasan, Ganesh Ramakrishnan
Parameter Screening and Optimisation for ILP using Designed Experiments. J. Mach. Learn. Res. 12 (2011)
Ashwin Srinivasan, Michael Bain
Knowledge-Guided Identification of Petri Net Models of Large Biological Systems. ILP (2011)
George Macleod Coghill, Ross D. King, Ashwin Srinivasan
Qualitative System Identification from Imperfect Data CoRR (2011)
Sumeet Agarwal, Candida Vaz, Alok Bhattacharya, Ashwin Srinivasan
Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM). BMC Bioinform. 11 (S-1) (2010)
Srihari Kalgi, Chirag Gosar, Prasad Gawde, Ganesh Ramakrishnan, Kekin Gada, Chander Iyer, T. V. S. Kiran, Ashwin Srinivasan
BET : An Inductive Logic Programming Workbench. ILP (2010)
Lucia Specia, Ashwin Srinivasan, Sachindra Joshi, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes
An investigation into feature construction to assist word sense disambiguation. Mach. Learn. 76 (1) (2009)
Ashwin Srinivasan, Ganesh Ramakrishnan
Parameter Screening and Optimisation for ILP Using Designed Experiments. ILP (2009)
Nuno A. Fonseca, Ashwin Srinivasan, Fernando M. A. Silva, Rui Camacho
Parallel ILP for distributed-memory architectures. Mach. Learn. 74 (3) (2009)
Ashwin Srinivasan, Ross D. King
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. J. Mach. Learn. Res. 9 (2008)
George Macleod Coghill, Ashwin Srinivasan, Ross D. King
Qualitative System Identification from Imperfect Data. J. Artif. Intell. Res. 32 (2008)
Sachindra Joshi, Ganesh Ramakrishnan, Ashwin Srinivasan
Feature Construction Using Theory-Guided Sampling and Randomised Search. ILP (2008)
Lucia Specia, Maria das Graças Volpe Nunes, Ashwin Srinivasan, Ganesh Ramakrishnan
USP-IBM-1 and USP-IBM-2: The ILP-based Systems for Lexical Sample WSD in SemEval-2007. SemEval@ACL (2007)
Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Balakrishnan, Ashwin Srinivasan
Using ILP to Construct Features for Information Extraction from Semi-structured Text. ILP (2007)
Filip Zelezný, Ashwin Srinivasan, C. David Page Jr.
Randomised restarted search in ILP. Mach. Learn. 64 (1-3) (2006)
Ashwin Srinivasan, David Page, Rui Camacho, Ross D. King
Quantitative pharmacophore models with inductive logic programming. Mach. Learn. 64 (1-3) (2006)
Aline Paes, Filip Zelezný, Gerson Zaverucha, C. David Page Jr., Ashwin Srinivasan
ILP Through Propositionalization and Stochastic k-Term DNF Learning. ILP (2006)
Lucia Specia, Ashwin Srinivasan, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes
Word Sense Disambiguation Using Inductive Logic Programming. ILP (2006)
Rui Camacho, Ross D. King, Ashwin Srinivasan
Guest editorial. Mach. Learn. 64 (1-3) (2006)
Hendrik Blockeel, David Page, Ashwin Srinivasan
Multi-instance tree learning. ICML (2005)
Ashwin Srinivasan, Ravi Kothari
A Study of Applying Dimensionality Reduction to Restrict the Size of a Hypothesis Space. ILP (2005)
Ashwin Srinivasan
Five Problems in Five Areas for Five Years. ILP (2005)
Filip Zelezný, Ashwin Srinivasan, David Page
A Monte Carlo Study of Randomised Restarted Search in ILP. ILP (2004)
Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer
Query Transformations for Improving the Efficiency of ILP Systems. J. Mach. Learn. Res. 4 (2003)
Ashwin Srinivasan, Ross D. King, Michael Bain
An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. J. Mach. Learn. Res. 4 (2003)
David Page, Ashwin Srinivasan
ILP: A Short Look Back and a Longer Look Forward. J. Mach. Learn. Res. 4 (2003)
Hannu Toivonen, Ashwin Srinivasan, Ross D. King, Stefan Kramer, Christoph Helma
Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinform. 19 (10) (2003)
Filip Zelezný, Ashwin Srinivasan, David Page
Lattice-Search Runtime Distributions May Be Heavy-Tailed. ILP (2002)
Ashwin Srinivasan
The Applicability to ILP of Results Concerning the Ordering of Binomial Populations. ILP (2002)
Christoph Helma, Ross D. King, Stefan Kramer, Ashwin Srinivasan
The Predictive Toxicology Challenge 2000-2001. Bioinform. 17 (1) (2001)
Ashwin Srinivasan
Extracting Context-Sensitive Models in Inductive Logic Programming. Mach. Learn. 44 (3) (2001)
Stephen Muggleton, Christopher H. Bryant, Ashwin Srinivasan, Alex Whittaker, Simon Topp, Christopher J. Rawlings
Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study. J. Comput. Biol. 8 (5) (2001)
Steve Moyle, Ashwin Srinivasan
Classificatory challenge-data mining: a recipe. Informatica (Slovenia) 25 (3) (2001)
Ross D. King, Ashwin Srinivasan, Luc Dehaspe
Warmr: a data mining tool for chemical data. J. Comput. Aided Mol. Des. 15 (2) (2001)
Stephen Muggleton, Christopher H. Bryant, Ashwin Srinivasan
Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study. ECML (2000)
Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System. ILP (2000)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ashwin Srinivasan