Search Results for author:
Found 42 papers, 8 papers with code
Predicting Citi Bike Demand Evolution Using Dynamic Graphs
In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
Hierarchical Termination Analysis for Generalized Planning
This paper presents a new approach for analyzing and identifying potentially useful generalized plans.
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning
In many real-world problems, the learning agent needs to learn a problem's abstractions and solution simultaneously.
Multi-Task Option Learning and Discovery for Stochastic Path Planning
This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems.
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons.
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs).
Differential Assessment of Black-Box AI Agents
In this work, we propose a novel approach to "differentially" assess black-box AI agents that have drifted from their previously known models.
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability.
Beyond Mono to Binaural: Generating Binaural Audio from Mono Audio with Depth and Cross Modal Attention
In this work, we argue that depth map of the scene can act as a proxy for inducing distance information of different objects in the scene, for the task of audio binauralization.
JEDAI: A System for Skill-Aligned Explainable Robot Planning
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts.
An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
We present a new approach for integrated task and motion planning in stochastic settings.
Learning Causal Models of Autonomous Agents using Interventions
One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems.
Depth Infused Binaural Audio Generation using Hierarchical Cross-Modal Attention
Binaural audio gives the listener the feeling of being in the recording place and enhances the immersive experience if coupled with AR/VR.
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions.
Planning for Proactive Assistance in Environments with Partial Observability
This paper addresses the problem of synthesizing the behavior of an AI agent that provides proactive task assistance to a human in settings like factory floors where they may coexist in a common environment.
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks
As a second contribution, we propose to improve the graph construction for GNNs for 3D point clouds.
Beyond Image to Depth: Improving Depth Prediction using Echoes
We propose a novel multi modal fusion technique, which incorporates the material properties explicitly while combining audio (echoes) and visual modalities to predict the scene depth.
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
Computing goal-directed behavior is essential to designing efficient AI systems.
Analysing Risk of Coronary Heart Disease through Discriminative Neural Networks
Such a model does not work on a probability-based approach to classify samples into labels.
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions.
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act.
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility
In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities.
Anytime Integrated Task and Motion Policies for Stochastic Environments
We present a new approach for integrated task and motion planning in stochastic settings.
Few Shot Speaker Recognition using Deep Neural Networks
The recent advances in deep learning are mostly driven by availability of large amount of training data.
An Online Learning Approach for Dengue Fever Classification
This paper introduces a novel approach for dengue fever classification based on online learning paradigms.
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios.
3D Object Detection
3D Object Detection From Monocular Images
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DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds
The method constitutes a deep network for learning permutation invariant representation of 3D points.
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice
Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans.
Learn and Link: Learning Critical Regions for Efficient Planning
We introduce a new suite of sampling-based motion planners, Learn and Link.
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements.
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users.
A Unified Framework for Planning in Adversarial and Cooperative Environments
By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals.
An Anytime Algorithm for Task and Motion MDPs
We present a new approach where the high-level decision problem occurs in a stochastic setting and can be modeled as a Markov decision process.
Object Classification using Ensemble of Local and Deep Features
In this paper we propose an ensemble of local and deep features for object classification.
Enhanced Characterness for Text Detection in the Wild
We show that a simple combination of characterness cues help in rejecting the non text regions.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Drought Stress Classification using 3D Plant Models
Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner.
3D Binary Signatures
3DBS describes keypoints from point clouds with a binary vector resulting in extremely fast matching.
Learning Generalized Reactive Policies using Deep Neural Networks
We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances.
Large Scale Novel Object Discovery in 3D
We test on unknown objects, which were not seen during training, and perform clustering in the learned embedding space of supervoxels to effectively perform novel object discovery.
Benchmarking KAZE and MCM for Multiclass Classification
In this paper, we propose a novel approach for feature generation by appropriately fusing KAZE and SIFT features.
SIFT Vs SURF: Quantifying the Variation in Transformations
This paper studies the robustness of SIFT and SURF against different image transforms (rigid body, similarity, affine and projective) by quantitatively analyzing the variations in the extent of transformations.
