Devendra Singh Dhami

Orcid: 0000-0002-4331-7193

According to our database1, Devendra Singh Dhami authored at least 66 papers between 2017 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
APT: Alarm Prediction Transformer.
Expert Syst. Appl., 2025

2024
Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images.
Remote. Sens., January, 2024

Graph Embedding Techniques for Predicting Missing Links in Biological Networks: An Empirical Evaluation.
IEEE Trans. Emerg. Top. Comput., 2024

Structural causal models reveal confounder bias in linear program modelling.
Mach. Learn., 2024

Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
CoRR, 2024

xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories.
CoRR, 2024

Systems with Switching Causal Relations: A Meta-Causal Perspective.
CoRR, 2024

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning.
CoRR, 2024

χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains.
CoRR, 2024

Towards Probabilistic Clearance, Explanation and Optimization.
CoRR, 2024

EXPIL: Explanatory Predicate Invention for Learning in Games.
CoRR, 2024

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Program.
CoRR, 2024

United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once.
CoRR, 2024

DeiSAM: Segment Anything with Deictic Prompting.
CoRR, 2024

Pix2Code: Learning to Compose Neural Visual Concepts as Programs.
CoRR, 2024

Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
αILP: thinking visual scenes as differentiable logic programs.
Mach. Learn., May, 2023

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal.
Trans. Mach. Learn. Res., 2023

Not All Causal Inference is the Same.
Trans. Mach. Learn. Res., 2023

Scalable Neural-Probabilistic Answer Set Programming.
J. Artif. Intell. Res., 2023

Learning Differentiable Logic Programs for Abstract Visual Reasoning.
CoRR, 2023

V-LoL: A Diagnostic Dataset for Visual Logical Learning.
CoRR, 2023

Do Not Marginalize Mechanisms, Rather Consolidate!
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes.
Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, 2023

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Vision Relation Transformer for Unbiased Scene Graph Generation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program.
Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges.
CoRR, 2022

Differentiable Meta logical Programming.
CoRR, 2022

LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems.
CoRR, 2022

HANF: Hyperparameter And Neural Architecture Search in Federated Learning.
CoRR, 2022

Can Foundation Models Talk Causality?
CoRR, 2022

Attributions Beyond Neural Networks: The Linear Program Case.
CoRR, 2022

Towards a Solution to Bongard Problems: A Causal Approach.
CoRR, 2022

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation.
CoRR, 2022

Machines Explaining Linear Programs.
CoRR, 2022

Finding Structure and Causality in Linear Programs.
CoRR, 2022

Predictive Whittle networks for time series.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Neural-Probabilistic Answer Set Programming.
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022

Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming.
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022

DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs.
Proceedings of the Artificial Intelligence in Medicine, 2022

2021
The Causal Loss: Driving Correlation to Imply Causation.
CoRR, 2021

On the Tractability of Neural Causal Inference.
CoRR, 2021

Neuro-Symbolic Forward Reasoning.
CoRR, 2021

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming.
CoRR, 2021

Structural Causal Interpretation Theorem.
CoRR, 2021

Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits.
CoRR, 2021

Relating Graph Neural Networks to Structural Causal Models.
CoRR, 2021

Intriguing Parameters of Structural Causal Models.
CoRR, 2021

Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN.
CoRR, 2021

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation.
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, 2021

Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.
Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem.
Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning.
Proceedings of the CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD, 2021

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach.
Proceedings of the Artificial Intelligence in Medicine, 2021

2020
Non-Parametric Learning of Gaifman Models.
CoRR, 2020

Knowledge Intensive Learning of Generative Adversarial Networks.
Proceedings of the ACM SIGKDD Workshop on Knowledge-infused Mining and Learning for Social Impact co-located with 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Virtual) (SIGKDD 2020), 2020

The Curious Case of Stacking Boosted Relational Dependency Networks.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019
Predicting Drug-Drug Interactions from Molecular Structure Images.
CoRR, 2019

Knowledge-augmented Column Networks: Guiding Deep Learning with Advice.
CoRR, 2019

Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice.
CoRR, 2019

Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2017
Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.
Proceedings of the Artificial Intelligence in Medicine, 2017

Knowledge-Based Morphological Classification of Galaxies from Vision Features.
Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017


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