Ashwin Srinivasan

According to our database, Ashwin Srinivasan authored at least 113 papers between 1992 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Composition of relational features with an application to explaining black-box predictors.

Ashwin Srinivasan, A. Baskar, Tirtharaj Dash, Devanshu Shah

Mach. Learn., 2024

Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback.

Shreyas Bhat Brahmavar, Ashwin Srinivasan, Tirtharaj Dash, Sowmya Ramaswamy Krishnan, Lovekesh Vig, Arijit Roy, Raviprasad Aduri

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Domain-Specific Pretraining Improves Confidence in Whole Slide Image Classification.

Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan

CoRR, 2023

Neuro-symbolic Meta Reinforcement Learning for Trading.

S. I Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig

CoRR, 2023

A Protocol for Intelligible Interaction Between Agents That Learn and Explain.

Ashwin Srinivasan, Michael Bain, A. Baskar, Enrico W. Coiera

CoRR, 2023

Can LLMs solve generative visual analogies?

Shrey Pandit, Gautam Shroff, Ashwin Srinivasan, Lovekesh Vig

Proceedings of the Workshop on the Interactions between Analogical Reasoning and Machine Learning co-located with International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023

IKD+: Reliable Low Complexity Deep Models for Retinopathy Classification.

Shreyas Bhat Brahmavar, Rohit Rajesh, Tirtharaj Dash, Lovekesh Vig, Tanmay Tulsidas Verlekar, Md Mahmudul Hasan, Tariq Mahmood Khan, Erik Meijering, Ashwin Srinivasan

Proceedings of the IEEE International Conference on Image Processing, 2023

Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification.

Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan

Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

2022

Learning explanations for biological feedback with delays using an event calculus.

Ashwin Srinivasan, Michael Bain, A. Baskar

Mach. Learn., 2022

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment.

Tirtharaj Dash, Ashwin Srinivasan, A. Baskar

Mach. Learn., 2022

Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss.

Vedant Shah, Aditya Agrawal, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Tanmay T. Verlekar

CoRR, 2022

One-way Explainability Isn't The Message.

Ashwin Srinivasan, Michael Bain, Enrico W. Coiera

CoRR, 2022

Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces.

Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan

Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 2nd International Joint Conference on Learning & Reasoning (IJCLR 2022), 2022

A Program-Synthesis Challenge for ARC-Like Tasks.

Aditya Challa, Ashwin Srinivasan, Michael Bain, Gautam Shroff

Proceedings of the Inductive Logic Programming - 31st International Conference, 2022

Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract).

Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Incorporating symbolic domain knowledge into graph neural networks.

Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig

Mach. Learn., 2021

Solving Visual Analogies Using Neural Algorithmic Reasoning.

Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash

CoRR, 2021

Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems.

Atharv Sonwane, Sharad Chitlangia, Tirtharaj Dash, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan

CoRR, 2021

How to Tell Deep Neural Networks What We Know.

Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan

CoRR, 2021

Incorporating Domain Knowledge into Deep Neural Networks.

Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan

CoRR, 2021

Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design.

Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Arijit Roy

Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

Empirical Study of Data-Free Iterative Knowledge Distillation.

Het Shah, Ashwin Vaswani, Tirtharaj Dash, Ramya Hebbalaguppe, Ashwin Srinivasan

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

2020

Constructing generative logical models for optimisation problems using domain knowledge.

Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff

Mach. Learn., 2020

Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays.

Rishab Khincha, Soundarya Krishnan, Krishnan Guru-Murthy, Tirtharaj Dash, Lovekesh Vig, Ashwin Srinivasan

CoRR, 2020

CovidDiagnosis: Deep Diagnosis of COVID-19 Patients Using Chest X-Rays.

Kushagra Mahajan, Monika Sharma, Lovekesh Vig, Rishab Khincha, Soundarya Krishnan, Adithya Niranjan, Tirtharaj Dash, Ashwin Srinivasan, Gautam Shroff

Proceedings of the Thoracic Image Analysis - Second International Workshop, 2020

A Case Study of Transfer of Lesion-Knowledge.

Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression.

Sharan Yalburgi, Tirtharaj Dash, Ramya Hebbalaguppe, Srinidhi Hegde, Ashwin Srinivasan

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Information Extraction from Document Images via FCA based Template Detection and Knowledge Graph Rule Induction.

Mouli Rastogi, Syed Afshan Ali, Mrinal Rawat, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences.

Sanket Rajan Gupte, Dharm Skandh Jain, Ashwin Srinivasan, Raviprasad Aduri

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

2019

Logical Explanations for Deep Relational Machines Using Relevance Information.

Ashwin Srinivasan, Lovekesh Vig, Michael Bain

J. Mach. Learn. Res., 2019

One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis.

Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul

Proceedings of the 2019 International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2019), 2019

Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines.

Tirtharaj Dash, Ashwin Srinivasan, Ramprasad S. Joshi, A. Baskar

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning, 2019

A Reinforcement Learning Framework for Container Selection and Ship Load Sequencing in Ports.

Richa Verma, Sarmimala Saikia, Harshad Khadilkar, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Consensus-based modeling using distributed feature construction with ILP.

Haimonti Dutta, Ashwin Srinivasan

Mach. Learn., 2018

Identification of biological transition systems using meta-interpreted logic programs.

Michael Bain, Ashwin Srinivasan

Mach. Learn., 2018

Large-Scale Assessment of Deep Relational Machines.

Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Oghenejokpeme I. Orhobor, Ross D. King

Proceedings of the Inductive Logic Programming - 28th International Conference, 2018

Evolutionary RL for Container Loading.

Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Language.

Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan

Proceedings of the Computer Vision - ACCV 2018 Workshops, 2018

2017

An empirical study of on-line models for relational data streams.

Ashwin Srinivasan, Michael Bain

Mach. Learn., 2017

An Investigation into the Role of Domain-Knowledge on the Use of Embeddings.

Lovekesh Vig, Ashwin Srinivasan, Michael Bain, Ankit Verma

Proceedings of the Inductive Logic Programming - 27th International Conference, 2017

Hybrid BiLSTM-Siamese network for FAQ Assistance.

Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016

ILP-assisted de novo drug design.

Rama Kaalia, Ashwin Srinivasan, Amit Kumar, Indira Ghosh

Mach. Learn., 2016

Generation of Near-Optimal Solutions Using ILP-Guided Sampling.

Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Puneet Agarwal

CoRR, 2016

Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs.

Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat

CoRR, 2016

Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs.

Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat

Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), 2016

Generation of Near-Optimal Solutions Using ILP-Guided Sampling.

Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia

Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

Learning transition models of biological regulatory and signaling networks from noisy data.

Deepika Vatsa, Sumeet Agarwal, Ashwin Srinivasan

Proceedings of the 3rd IKDD Conference on Data Science, 2016

2015

Identification of Transition Models of Biological Systems in the Presence of Transition Noise.

Ashwin Srinivasan, Michael Bain, Deepika Vatsa, Sumeet Agarwal

Proceedings of the Inductive Logic Programming - 25th International Conference, 2015

Succinctly summarizing machine usage via multi-subspace clustering of multi-sensor data.

Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

2014

Consensus-Based Modelling using Distributed Feature Construction.

Haimonti Dutta, Ashwin Srinivasan

CoRR, 2014

Interactively Visualizing Summaries of Rules and Exceptions.

Geetika Sharma, Gautam Shroff, Aditeya Pandey, Puneet Agarwal, Ashwin Srinivasan

Proceedings of the 5th International EuroVis Workshop on Visual Analytics, 2014

Exploratory Data Analysis Using Alternating Covers of Rules and Exceptions.

Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan, Aditeya Pandey, Gaurangi Anand

Proceedings of the 20th International Conference on Management of Data, 2014

2012

Data and task parallelism in ILP using MapReduce.

Ashwin Srinivasan, Tanveer A. Faruquie, Sachindra Joshi

Mach. Learn., 2012

ILP turns 20 - Biography and future challenges.

Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan

Mach. Learn., 2012

What Kinds of Relational Features Are Useful for Statistical Learning?

Amrita Saha, Ashwin Srinivasan, Ganesh Ramakrishnan

Proceedings of the Inductive Logic Programming - 22nd International Conference, 2012

Topic Models with Relational Features for Drug Design.

Tanveer A. Faruquie, Ashwin Srinivasan, Ross D. King

Proceedings of the Inductive Logic Programming - 22nd International Conference, 2012

2011

Parameter Screening and Optimisation for ILP using Designed Experiments.

Ashwin Srinivasan, Ganesh Ramakrishnan

J. Mach. Learn. Res., 2011

Knowledge-Guided Identification of Petri Net Models of Large Biological Systems.

Ashwin Srinivasan, Michael Bain

Proceedings of the Inductive Logic Programming - 21st International Conference, 2011

2010

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM).

Sumeet Agarwal, Candida Vaz, Alok Bhattacharya, Ashwin Srinivasan

BMC Bioinform., 2010

BET : An Inductive Logic Programming Workbench.

Srihari Kalgi, Chirag Gosar, Prasad Gawde, Ganesh Ramakrishnan, Kekin Gada, Chander Iyer, T. V. S. Kiran, Ashwin Srinivasan

Proceedings of the Inductive Logic Programming - 20th International Conference, 2010

2009

An investigation into feature construction to assist word sense disambiguation.

Lucia Specia, Ashwin Srinivasan, Sachindra Joshi, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes

Mach. Learn., 2009

Parallel ILP for distributed-memory architectures.

Nuno A. Fonseca, Ashwin Srinivasan, Fernando M. A. Silva, Rui Camacho

Mach. Learn., 2009

2008

Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming.

Ashwin Srinivasan, Ross D. King

J. Mach. Learn. Res., 2008

Qualitative System Identification from Imperfect Data.

George Macleod Coghill, Ashwin Srinivasan, Ross D. King

J. Artif. Intell. Res., 2008

Feature Construction Using Theory-Guided Sampling and Randomised Search.

Sachindra Joshi, Ganesh Ramakrishnan, Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 18th International Conference, 2008

2007

USP-IBM-1 and USP-IBM-2: The ILP-based Systems for Lexical Sample WSD in SemEval-2007.

Lucia Specia, Maria das Graças Volpe Nunes, Ashwin Srinivasan, Ganesh Ramakrishnan

Proceedings of the 4th International Workshop on Semantic Evaluations, 2007

Using ILP to Construct Features for Information Extraction from Semi-structured Text.

Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Balakrishnan, Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 17th International Conference, 2007

Learning Qualitative Models of Physical and Biological Systems.

Simon M. Garrett, George Macleod Coghill, Ashwin Srinivasan, Ross D. King

Proceedings of the Computational Discovery of Scientific Knowledge, 2007

2006

Randomised restarted search in ILP.

Filip Zelezný, Ashwin Srinivasan, C. David Page Jr.

Mach. Learn., 2006

Quantitative pharmacophore models with inductive logic programming.

Ashwin Srinivasan, David Page, Rui Camacho, Ross D. King

Mach. Learn., 2006

Guest editorial.

Rui Camacho, Ross D. King, Ashwin Srinivasan

Mach. Learn., 2006

Word Sense Disambiguation Using Inductive Logic Programming.

Lucia Specia, Ashwin Srinivasan, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes

Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

ILP Through Propositionalization and Stochastic k-Term DNF Learning.

Aline Paes, Filip Zelezný, Gerson Zaverucha, C. David Page Jr., Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

2005

A Study of Applying Dimensionality Reduction to Restrict the Size of a Hypothesis Space.

Ashwin Srinivasan, Ravi Kothari

Proceedings of the Inductive Logic Programming, 15th International Conference, 2005

Five Problems in Five Areas for Five Years.

Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 15th International Conference, 2005

Multi-instance tree learning.

Hendrik Blockeel, David Page, Ashwin Srinivasan

Proceedings of the Machine Learning, 2005

2004

A Monte Carlo Study of Randomised Restarted Search in ILP.

Filip Zelezný, Ashwin Srinivasan, David Page

Proceedings of the Inductive Logic Programming, 14th International Conference, 2004

2003

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming.

Ashwin Srinivasan, Ross D. King, Michael Bain

J. Mach. Learn. Res., 2003

ILP: A Short Look Back and a Longer Look Forward.

David Page, Ashwin Srinivasan

J. Mach. Learn. Res., 2003

Query Transformations for Improving the Efficiency of ILP Systems.

Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer

J. Mach. Learn. Res., 2003

Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001.

Hannu Toivonen, Ashwin Srinivasan, Ross D. King, Stefan Kramer, Christoph Helma

Bioinform., 2003

2002

Lattice-Search Runtime Distributions May Be Heavy-Tailed.

Filip Zelezný, Ashwin Srinivasan, David Page

Proceedings of the Inductive Logic Programming, 12th International Conference, 2002

The Applicability to ILP of Results Concerning the Ordering of Binomial Populations.

Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 12th International Conference, 2002

2001

Extracting Context-Sensitive Models in Inductive Logic Programming.

Ashwin Srinivasan

Mach. Learn., 2001

Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan, Alex Whittaker, Simon Topp, Christopher J. Rawlings

J. Comput. Biol., 2001

Warmr: a data mining tool for chemical data.

Ross D. King, Ashwin Srinivasan, Luc Dehaspe

J. Comput. Aided Mol. Des., 2001

Classificatory challenge-data mining: a recipe.

Steve Moyle, Ashwin Srinivasan

Informatica (Slovenia), 2001

The Predictive Toxicology Challenge 2000-2001.

Christoph Helma, Ross D. King, Stefan Kramer, Ashwin Srinivasan

Bioinform., 2001

2000

A Note on Two Simple Transformations for Improving the Efficiency of an ILP System.

Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho

Proceedings of the Inductive Logic Programming, 10th International Conference, 2000

Discovering the Structure of Partial Differential Equations from Example Behaviour.

Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan P. Whiteley, David Gavaghan

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Learning Chomsky-like Grammars for Biological Sequence Families.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan

Proceedings of the Machine Learning: ECML 2000, 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31, 2000

1999

Numerical Reasoning with an ILP System Capable of Lazy Evaluation and Customised Search.

Ashwin Srinivasan, Rui Camacho

J. Log. Program., 1999

Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes.

Ashwin Srinivasan, Ross D. King

Data Min. Knowl. Discov., 1999

A Study of Two Sampling Methods for Analyzing Large Datasets with ILP.

Ashwin Srinivasan

Data Min. Knowl. Discov., 1999

An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment.

Ashwin Srinivasan, Ross D. King, Douglas W. Bristol

Proceedings of the Inductive Logic Programming, 9th International Workshop, 1999

An assessment of submissions made to the Predictive Toxicology Evaluation Challenge.

Ashwin Srinivasan, Ross D. King, Douglas W. Bristol

Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999

1998

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL.

Paul W. Finn, Stephen H. Muggleton, David Page, Ashwin Srinivasan

Mach. Learn., 1998

The discovery of indicator variables for QSAR using inductive logic programming.

Ross D. King, Ashwin Srinivasan

J. Comput. Aided Mol. Des., 1998

Application of ILP to Problems in Chemistry and Biology (Abstract).

Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 8th International Workshop, 1998

Biochemical Knowledge Discovery Using Inductive Logic Programming.

Stephen H. Muggleton, Ashwin Srinivasan, Ross D. King, Michael J. E. Sternberg

Proceedings of the Discovery Science, 1998

1997

Carcinogenesis Predictions Using ILP.

Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg

Proceedings of the Inductive Logic Programming, 7th International Workshop, 1997

The Predictive Toxicology Evaluation Challenge.

Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg

Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

1996

Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction.

Ashwin Srinivasan, Stephen H. Muggleton, Michael J. E. Sternberg, Ross D. King

Artif. Intell., 1996

Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes.

Ashwin Srinivasan, Ross D. King

Proceedings of the Inductive Logic Programming, 6th International Workshop, 1996

An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming.

Stephen H. Muggleton, David Page, Ashwin Srinivasan

Proceedings of the Inductive Logic Programming, 6th International Workshop, 1996

1995

Relating Chemical Activity to Structure: An Examination of ILP Successes.

Ross D. King, Michael J. E. Sternberg, Ashwin Srinivasan

New Gener. Comput., 1995

Drug Design by Machine Learning.

Michael J. E. Sternberg, Ross D. King, Ashwin Srinivasan, Stephen H. Muggleton

Proceedings of the Machine Intelligence 15, 1995

1993

Inductive Logic Programming With Large-Scale Unstructured Data.

Michael Bain, Ashwin Srinivasan

Proceedings of the Machine Intelligence 14, 1993

Generating Explicit Orderings for Non-monotonic Logics.

James Cussens, Anthony Hunter, Ashwin Srinivasan

Proceedings of the 11th National Conference on Artificial Intelligence. Washington, 1993

1992

Ripple down rules: Turning knowledge acquisition into knowledge maintenance.

Paul Compton, Glenn Edwards, Byeong Kang, Leslie Lazarus, Ron Malor, Phillip Preston, Ashwin Srinivasan

Artif. Intell. Medicine, 1992

The Justification of Logical Theories based on Data Compression.

Ashwin Srinivasan, Stephen H. Muggleton, Michael Bain

Proceedings of the Machine Intelligence 13, 1992

Compression, Significance, and Accuracy.

Stephen H. Muggleton, Ashwin Srinivasan, Michael Bain

Proceedings of the Ninth International Workshop on Machine Learning (ML 1992), 1992