Search Results for author: Devendra Singh Dhami

Found 35 papers, 12 papers with code

DeiSAM: Segment Anything with Deictic Prompting

1 code implementation21 Feb 2024 Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting

To remedy this issue, we propose DeiSAM -- a combination of large pre-trained neural networks with differentiable logic reasoners -- for deictic promptable segmentation.

Image Segmentation Segmentation +1

Pix2Code: Learning to Compose Neural Visual Concepts as Programs

1 code implementation13 Feb 2024 Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning.

Program Synthesis Relational Reasoning

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal

1 code implementation24 Aug 2023 Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting

We conjecture that in the cases where LLM succeed in doing causal inference, underlying was a respective meta SCM that exposed correlations between causal facts in natural language on whose data the LLM was ultimately trained.

Causal Inference

Vision Relation Transformer for Unbiased Scene Graph Generation

1 code implementation ICCV 2023 Gopika Sudhakaran, Devendra Singh Dhami, Kristian Kersting, Stefan Roth

Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict entity relationships using a relation encoder-decoder pipeline stacked on top of an object encoder-decoder backbone.

Graph Generation Relation +2

Learning Differentiable Logic Programs for Abstract Visual Reasoning

1 code implementation3 Jul 2023 Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting

However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve abstract visual reasoning, where agents need to perform reasoning by using analogies on abstract concepts in different scenarios.

Program induction Visual Reasoning

Scalable Neural-Probabilistic Answer Set Programming

1 code implementation14 Jun 2023 Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI.

Probabilistic Programming Question Answering +1

V-LoL: A Diagnostic Dataset for Visual Logical Learning

1 code implementation13 Jun 2023 Lukas Helff, Wolfgang Stammer, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to understanding complex and noisy scenes.

Logical Reasoning Visual Reasoning

Pearl Causal Hierarchy on Image Data: Intricacies & Challenges

no code implementations23 Dec 2022 Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting

Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems.

counterfactual

Neural Meta-Symbolic Reasoning and Learning

no code implementations21 Nov 2022 Zihan Ye, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

To make deep learning do more from less, we propose the first neural meta-symbolic system (NEMESYS) for reasoning and learning: meta programming using differentiable forward-chaining reasoning in first-order logic.

FEATHERS: Federated Architecture and Hyperparameter Search

no code implementations24 Jun 2022 Jonas Seng, Pooja Prasad, Martin Mundt, Devendra Singh Dhami, Kristian Kersting

Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience.

BIG-bench Machine Learning Federated Learning +3

Can Foundation Models Talk Causality?

1 code implementation14 Jun 2022 Moritz Willig, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities.

Attributions Beyond Neural Networks: The Linear Program Case

no code implementations14 Jun 2022 Florian Peter Busch, Matej Zečević, Kristian Kersting, Devendra Singh Dhami

We introduce an approach where we consider neural encodings for LPs that justify the application of attribution methods from explainable artificial intelligence (XAI) designed for neural learning systems.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Towards a Solution to Bongard Problems: A Causal Approach

no code implementations14 Jun 2022 Salahedine Youssef, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques.

Contrastive Learning reinforcement-learning +1

Machines Explaining Linear Programs

no code implementations14 Jun 2022 David Steinmann, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

In this work, we extend the attribution methods for explaining neural networks to linear programs.

Finding Structure and Causality in Linear Programs

1 code implementation29 Mar 2022 Matej Zečević, Florian Peter Busch, Devendra Singh Dhami, Kristian Kersting

Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems.

BIG-bench Machine Learning

The Causal Loss: Driving Correlation to Imply Causation

no code implementations22 Oct 2021 Moritz Willig, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance.

A Taxonomy for Inference in Causal Model Families

no code implementations22 Oct 2021 Matej Zečević, Devendra Singh Dhami, Kristian Kersting

More specifically, there are models capable of answering causal queries that are not SCM, which we refer to as \emph{partially causal models} (PCM).

Causal Inference

Neuro-Symbolic Forward Reasoning

1 code implementation18 Oct 2021 Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

NSFR factorizes the raw inputs into the object-centric representations, converts them into probabilistic ground atoms, and finally performs differentiable forward-chaining inference using weighted rules for inference.

Object

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming

no code implementations7 Oct 2021 Arseny Skryagin, Wolfgang Stammer, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

The probability estimates resulting from NPPs act as the binding element between the logical program and raw input data, thereby allowing SLASH to answer task-dependent logical queries.

Probabilistic Programming

Causal Explanations of Structural Causal Models

no code implementations5 Oct 2021 Matej Zečević, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting

The question part on the user's end we believe to be solved since the user's mental model can provide the causal model.

BIG-bench Machine Learning

Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits

no code implementations14 Sep 2021 Zhongjie Yu, Devendra Singh Dhami, Kristian Kersting

Probabilistic circuits (PCs) have become the de-facto standard for learning and inference in probabilistic modeling.

Relating Graph Neural Networks to Structural Causal Models

no code implementations9 Sep 2021 Matej Zečević, Devendra Singh Dhami, Petar Veličković, Kristian Kersting

Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations.

Causal Inference

Structural Causal Models Reveal Confounder Bias in Linear Program Modelling

1 code implementation26 May 2021 Matej Zečević, Devendra Singh Dhami, Kristian Kersting

The recent years have been marked by extended research on adversarial attacks, especially on deep neural networks.

Combinatorial Optimization

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

no code implementations19 Mar 2021 Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan

Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.

A Statistical Relational Approach to Learning Distance-based GCNs

no code implementations13 Feb 2021 Devendra Singh Dhami, Siwen Yan, Sriraam Natarajan

We consider the problem of learning distance-based Graph Convolutional Networks (GCNs) for relational data.

Density Estimation Link Prediction +2

The Curious Case of Stacking Boosted Relational Dependency Networks

no code implementations NeurIPS Workshop ICBINB 2020 Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan

Reducing bias while learning and inference is an important requirement to achieve generalizable and better performing models.

Relational Reasoning

Non-Parametric Learning of Gaifman Models

no code implementations2 Jan 2020 Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan

We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base.

Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks

no code implementations14 Nov 2019 Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan

Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view.

BIG-bench Machine Learning

Knowledge-augmented Column Networks: Guiding Deep Learning with Advice

no code implementations31 May 2019 Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan

Recently, deep models have had considerable success in several tasks, especially with low-level representations.

BIG-bench Machine Learning

Human-Guided Column Networks: Augmenting Deep Learning with Advice

no code implementations ICLR 2019 Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan

While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models.

Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice

no code implementations15 Apr 2019 Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan

Recently, deep models have been successfully applied in several applications, especially with low-level representations.

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