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ORCIDAshwin Srinivasan 0001
Person information
- affiliation: Birla Institute of Technology and Science, Goa, India
Other persons with the same name
- Ashwin Srinivasan — disambiguation page
- Ashwin Srinivasan 0002 — Carnegie Mellon University, PA, USA
- Ashwin Srinivasan 0003 — Microsoft
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2020 – today
- 2024
[j39]Ashwin Srinivasan
, A. Baskar, Tirtharaj Dash, Devanshu Shah:
Composition of relational features with an application to explaining black-box predictors. Mach. Learn. 113(3): 1091-1132 (2024)- [c62]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: 21-29 - 2023
- [c61]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: 1-4 - [c60]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: 2400-2404 - [c59]Shrey Pandit, Gautam Shroff, Ashwin Srinivasan, Lovekesh Vig:
Can LLMs solve generative visual analogies? IARML@IJCAI 2023: 30-32 - [i23]Ashwin Srinivasan, Michael Bain, A. Baskar, Enrico W. Coiera:
A Protocol for Intelligible Interaction Between Agents That Learn and Explain. CoRR abs/2301.01819 (2023) - [i22]S. I Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig:
Neuro-symbolic Meta Reinforcement Learning for Trading. CoRR abs/2302.08996 (2023) - [i21]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 abs/2302.09833 (2023) - [i20]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. CoRR abs/2303.02310 (2023) - 2022
- [j38]Tirtharaj Dash, Ashwin Srinivasan, A. Baskar:
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment. Mach. Learn. 111(2): 575-623 (2022) - [j37]Ashwin Srinivasan, Michael Bain, A. Baskar:
Learning explanations for biological feedback with delays using an event calculus. Mach. Learn. 111(7): 2435-2487 (2022) - [c58]Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash:
Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract). AAAI 2022: 13055-13056 - [c57]Aditya Challa, Ashwin Srinivasan, Michael Bain, Gautam Shroff:
A Program-Synthesis Challenge for ARC-Like Tasks. ILP 2022: 25-39 - [c56]Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan:
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces. NeSy 2022: 142-154 - [i19]Ashwin Srinivasan, Michael Bain, Enrico W. Coiera:
One-way Explainability Isn't The Message. CoRR abs/2205.08954 (2022) - [i18]Ashwin Srinivasan, A. Baskar, Tirtharaj Dash, Devanshu Shah:
Composition of Relational Features with an Application to Explaining Black-Box Predictors. CoRR abs/2206.00738 (2022) - [i17]Vishwa Shah, Aditya Sharma, Gautam Shroff, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan:
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces. CoRR abs/2209.08750 (2022) - [i16]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 abs/2211.16047 (2022) - 2021
- [j36]Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig:
Incorporating symbolic domain knowledge into graph neural networks. Mach. Learn. 110(7): 1609-1636 (2021) - [c55]Het Shah, Ashwin Vaswani, Tirtharaj Dash, Ramya Hebbalaguppe, Ashwin Srinivasan:
Empirical Study of Data-Free Iterative Knowledge Distillation. ICANN (3) 2021: 546-557 - [c54]Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Arijit Roy:
Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design. ILP 2021: 78-94 - [i15]Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan:
Incorporating Domain Knowledge into Deep Neural Networks. CoRR abs/2103.00180 (2021) - [i14]Tirtharaj Dash, Ashwin Srinivasan, A. Baskar:
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment. CoRR abs/2105.10709 (2021) - [i13]Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan:
How to Tell Deep Neural Networks What We Know. CoRR abs/2107.10295 (2021) - [i12]Atharv Sonwane, Sharad Chitlangia, Tirtharaj Dash, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan:
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems. CoRR abs/2110.09947 (2021) - [i11]Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash:
Solving Visual Analogies Using Neural Algorithmic Reasoning. CoRR abs/2111.10361 (2021) - 2020
- [j35]Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff:
Constructing generative logical models for optimisation problems using domain knowledge. Mach. Learn. 109(7): 1371-1392 (2020) - [c53]Sanket Rajan Gupte, Dharm Skandh Jain, Ashwin Srinivasan, Raviprasad Aduri:
MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences. BIBM 2020: 421-425 - [c52]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: 2377-2385 - [c51]Sharan Yalburgi, Tirtharaj Dash, Ramya Hebbalaguppe, Srinidhi Hegde, Ashwin Srinivasan:
An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression. ESANN 2020: 217-222 - [c50]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: 61-73 - [c49]Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan:
A Case Study of Transfer of Lesion-Knowledge. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 138-145 - [i10]Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig:
Incorporating Symbolic Domain Knowledge into Graph Neural Networks. CoRR abs/2010.13900 (2020) - [i9]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 abs/2012.10787 (2020)
2010 – 2019
- 2019
- [j34]Ashwin Srinivasan, Lovekesh Vig, Michael Bain:
Logical Explanations for Deep Relational Machines Using Relevance Information. J. Mach. Learn. Res. 20: 130:1-130:47 (2019) - [c48]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: 2250-2252 - [c47]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: 29-45 - [c46]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 - [i8]Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, Rohit Rahul:
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis. CoRR abs/1906.02427 (2019) - 2018
- [j33]Haimonti Dutta, Ashwin Srinivasan:
Consensus-based modeling using distributed feature construction with ILP. Mach. Learn. 107(5): 825-858 (2018) - [j32]Michael Bain, Ashwin Srinivasan:
Identification of biological transition systems using meta-interpreted logic programs. Mach. Learn. 107(7): 1171-1206 (2018) - [c45]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: 186-201 - [c44]Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan:
Evolutionary RL for Container Loading. ESANN 2018 - [c43]Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig, Oghenejokpeme I. Orhobor, Ross D. King:
Large-Scale Assessment of Deep Relational Machines. ILP 2018: 22-37 - [i7]Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan:
Evolutionary RL for Container Loading. CoRR abs/1805.06664 (2018) - [i6]Ashwin Srinivasan, Lovekesh Vig, Michael Bain:
Logical Explanations for Deep Relational Machines Using Relevance Information. CoRR abs/1807.00595 (2018) - [i5]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 abs/1812.04377 (2018) - 2017
- [j31]Ashwin Srinivasan, Michael Bain:
An empirical study of on-line models for relational data streams. Mach. Learn. 106(2): 243-276 (2017) - [c42]Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan:
Hybrid BiLSTM-Siamese network for FAQ Assistance. CIKM 2017: 537-545 - [c41]Lovekesh Vig, Ashwin Srinivasan, Michael Bain, Ankit Verma:
An Investigation into the Role of Domain-Knowledge on the Use of Embeddings. ILP 2017: 169-183 - 2016
- [j30]Rama Kaalia, Ashwin Srinivasan, Amit Kumar, Indira Ghosh:
ILP-assisted de novo drug design. Mach. Learn. 103(3): 309-341 (2016) - [c40]Deepika Vatsa, Sumeet Agarwal, Ashwin Srinivasan:
Learning transition models of biological regulatory and signaling networks from noisy data. CODS 2016: 9:1-9:6 - [c39]Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia:
Generation of Near-Optimal Solutions Using ILP-Guided Sampling. ILP 2016: 120-131 - [c38]Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat:
Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs. CoCo@NIPS 2016 - [i4]Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia, Puneet Agarwal:
Generation of Near-Optimal Solutions Using ILP-Guided Sampling. CoRR abs/1608.01093 (2016) - [i3]Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat:
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs. CoRR abs/1612.06528 (2016) - 2015
- [c37]Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan:
Succinctly summarizing machine usage via multi-subspace clustering of multi-sensor data. DSAA 2015: 1-10 - [c36]Ashwin Srinivasan, Michael Bain, Deepika Vatsa, Sumeet Agarwal:
Identification of Transition Models of Biological Systems in the Presence of Transition Noise. ILP 2015: 200-214 - 2014
- [c35]Sarmimala Saikia, Gautam Shroff, Puneet Agarwal, Ashwin Srinivasan, Aditeya Pandey, Gaurangi Anand:
Exploratory Data Analysis Using Alternating Covers of Rules and Exceptions. COMAD 2014: 105-108 - [c34]Geetika Sharma, Gautam Shroff, Aditeya Pandey, Puneet Agarwal, Ashwin Srinivasan:
Interactively Visualizing Summaries of Rules and Exceptions. EuroVA@EuroVis 2014 - [i2]Haimonti Dutta, Ashwin Srinivasan:
Consensus-Based Modelling using Distributed Feature Construction. CoRR abs/1409.3446 (2014) - 2012
- [j29]Stephen H. 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): 3-23 (2012) - [j28]Ashwin Srinivasan, Tanveer A. Faruquie, Sachindra Joshi:
Data and task parallelism in ILP using MapReduce. Mach. Learn. 86(1): 141-168 (2012) - [c33]Tanveer A. Faruquie, Ashwin Srinivasan, Ross D. King:
Topic Models with Relational Features for Drug Design. ILP 2012: 45-57 - [c32]Amrita Saha, Ashwin Srinivasan, Ganesh Ramakrishnan:
What Kinds of Relational Features Are Useful for Statistical Learning? ILP 2012: 209-224 - 2011
- [j27]Ashwin Srinivasan, Ganesh Ramakrishnan:
Parameter Screening and Optimisation for ILP using Designed Experiments. J. Mach. Learn. Res. 12: 627-662 (2011) - [c31]Ashwin Srinivasan, Michael Bain:
Knowledge-Guided Identification of Petri Net Models of Large Biological Systems. ILP 2011: 317-331 - [i1]George Macleod Coghill, Ross D. King, Ashwin Srinivasan:
Qualitative System Identification from Imperfect Data. CoRR abs/1111.0051 (2011) - 2010
- [j26]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): 29 (2010) - [c30]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: 130-137
2000 – 2009
- 2009
- [j25]Nuno A. Fonseca, Ashwin Srinivasan, Fernando M. A. Silva, Rui Camacho:
Parallel ILP for distributed-memory architectures. Mach. Learn. 74(3): 257-279 (2009) - [j24]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): 109-136 (2009) - [c29]Ashwin Srinivasan, Ganesh Ramakrishnan:
Parameter Screening and Optimisation for ILP Using Designed Experiments. ILP 2009: 217-225 - 2008
- [j23]George Macleod Coghill, Ashwin Srinivasan, Ross D. King:
Qualitative System Identification from Imperfect Data. J. Artif. Intell. Res. 32: 825-877 (2008) - [j22]Ashwin Srinivasan, Ross D. King:
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. J. Mach. Learn. Res. 9: 1475-1533 (2008) - [c28]Sachindra Joshi, Ganesh Ramakrishnan, Ashwin Srinivasan:
Feature Construction Using Theory-Guided Sampling and Randomised Search. ILP 2008: 140-157 - 2007
- [c27]Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Balakrishnan, Ashwin Srinivasan:
Using ILP to Construct Features for Information Extraction from Semi-structured Text. ILP 2007: 211-224 - [c26]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: 442-445 - [p1]Simon M. Garrett, George Macleod Coghill, Ashwin Srinivasan, Ross D. King:
Learning Qualitative Models of Physical and Biological Systems. Computational Discovery of Scientific Knowledge 2007: 248-272 - 2006
- [j21]Ashwin Srinivasan, David Page, Rui Camacho, Ross D. King:
Quantitative pharmacophore models with inductive logic programming. Mach. Learn. 64(1-3): 65-90 (2006) - [j20]Rui Camacho, Ross D. King, Ashwin Srinivasan:
Guest editorial. Mach. Learn. 64(1-3): 145-147 (2006) - [j19]Filip Zelezný, Ashwin Srinivasan, C. David Page Jr.:
Randomised restarted search in ILP. Mach. Learn. 64(1-3): 183-208 (2006) - [c25]Aline Paes, Filip Zelezný, Gerson Zaverucha, C. David Page Jr., Ashwin Srinivasan:
ILP Through Propositionalization and Stochastic k-Term DNF Learning. ILP 2006: 379-393 - [c24]Lucia Specia, Ashwin Srinivasan, Ganesh Ramakrishnan, Maria das Graças Volpe Nunes:
Word Sense Disambiguation Using Inductive Logic Programming. ILP 2006: 409-423 - 2005
- [c23]Hendrik Blockeel, David Page, Ashwin Srinivasan:
Multi-instance tree learning. ICML 2005: 57-64 - [c22]Ashwin Srinivasan, Ravi Kothari:
A Study of Applying Dimensionality Reduction to Restrict the Size of a Hypothesis Space. ILP 2005: 348-365 - [c21]Ashwin Srinivasan:
Five Problems in Five Areas for Five Years. ILP 2005: 424-425 - 2004
- [c20]Filip Zelezný, Ashwin Srinivasan, David Page:
A Monte Carlo Study of Randomised Restarted Search in ILP. ILP 2004: 341-358 - [e1]Rui Camacho, Ross D. King, Ashwin Srinivasan:
Inductive Logic Programming, 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004, Proceedings. Lecture Notes in Computer Science 3194, Springer 2004, ISBN 3-540-22941-8 [contents] - 2003
- [j18]Hannu Toivonen, Ashwin Srinivasan, Ross D. King, Stefan Kramer, Christoph Helma:
Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinform. 19(10): 1183-1193 (2003) - [j17]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: 369-383 (2003) - [j16]David Page, Ashwin Srinivasan:
ILP: A Short Look Back and a Longer Look Forward. J. Mach. Learn. Res. 4: 415-430 (2003) - [j15]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: 465-491 (2003) - 2002
- [c19]Ashwin Srinivasan:
The Applicability to ILP of Results Concerning the Ordering of Binomial Populations. ILP 2002: 238-253 - [c18]Filip Zelezný, Ashwin Srinivasan, David Page:
Lattice-Search Runtime Distributions May Be Heavy-Tailed. ILP 2002: 333-345 - 2001
- [j14]Christoph Helma, Ross D. King, Stefan Kramer, Ashwin Srinivasan:
The Predictive Toxicology Challenge 2000-2001. Bioinform. 17(1): 107-108 (2001) - [j13]Steve Moyle, Ashwin Srinivasan:
Classificatory challenge-data mining: a recipe. Informatica (Slovenia) 25(3) (2001) - [j12]Ross D. King, Ashwin Srinivasan, Luc Dehaspe:
Warmr: a data mining tool for chemical data. J. Comput. Aided Mol. Des. 15(2): 173-181 (2001) - [j11]Stephen H. 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): 493-521 (2001) - [j10]Ashwin Srinivasan:
Extracting Context-Sensitive Models in Inductive Logic Programming. Mach. Learn. 44(3): 301-324 (2001) - 2000
- [c17]Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan:
Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study. ECML 2000: 300-312 - [c16]Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan:
Learning Chomsky-like Grammars for Biological Sequence Families. ICML 2000: 631-638 - [c15]Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan P. Whiteley, David Gavaghan:
Discovering the Structure of Partial Differential Equations from Example Behaviour. ICML 2000: 991-998 - [c14]Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho:
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System. ILP 2000: 225-242
1990 – 1999
- 1999
- [j9]Ashwin Srinivasan, Ross D. King:
Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes. Data Min. Knowl. Discov. 3(1): 37-57 (1999) - [j8]Ashwin Srinivasan:
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP. Data Min. Knowl. Discov. 3(1): 95-123 (1999) - [j7]Ashwin Srinivasan, Rui Camacho:
Numerical Reasoning with an ILP System Capable of Lazy Evaluation and Customised Search. J. Log. Program. 40(2-3): 185-213 (1999) - [c13]Ashwin Srinivasan, Ross D. King, Douglas W. Bristol:
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge. IJCAI 1999: 270-275 - [c12]Ashwin Srinivasan, Ross D. King, Douglas W. Bristol:
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment. ILP 1999: 291-302 - 1998
- [j6]Ross D. King, Ashwin Srinivasan:
The discovery of indicator variables for QSAR using inductive logic programming. J. Comput. Aided Mol. Des. 12(6): 571-580 (1998) - [j5]Paul W. Finn, Stephen H. Muggleton, David Page, Ashwin Srinivasan:
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL. Mach. Learn. 30(2-3): 241-270 (1998) - [c11]Stephen H. Muggleton, Ashwin Srinivasan, Ross D. King, Michael J. E. Sternberg:
Biochemical Knowledge Discovery Using Inductive Logic Programming. Discovery Science 1998: 326-341 - [c10]Ashwin Srinivasan:
Application of ILP to Problems in Chemistry and Biology (Abstract). ILP 1998: 10 - 1997
- [j4]Ross D. King, Ashwin Srinivasan:
The discovery of indicator variables for QSAR using inductive logic programming. J. Comput. Aided Mol. Des. 11(6): 571-580 (1997) - [c9]Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg:
The Predictive Toxicology Evaluation Challenge. IJCAI (1) 1997: 4-9 - [c8]Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg:
Carcinogenesis Predictions Using ILP. ILP 1997: 273-287 - 1996
- [j3]Ashwin Srinivasan, Stephen H. Muggleton, Michael J. E. Sternberg, Ross D. King:
Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction. Artif. Intell. 85(1-2): 277-299 (1996) - [c7]Stephen H. Muggleton, David Page, Ashwin Srinivasan:
An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming. Inductive Logic Programming Workshop 1996: 25-40 - [c6]Ashwin Srinivasan, Ross D. King:
Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes. Inductive Logic Programming Workshop 1996: 89-104 - 1995
- [j2]Ross D. King, Michael J. E. Sternberg, Ashwin Srinivasan:
Relating Chemical Activity to Structure: An Examination of ILP Successes. New Gener. Comput. 13(3&4): 411-433 (1995) - [c5]