Pratik Chaudhari

Orcid: 0000-0003-4590-1956

According to our database1, Pratik Chaudhari authored at least 66 papers between 2009 and 2023.

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Bibliography

2023
Time as a supervisor: temporal regularity and auditory object learning.
Frontiers Comput. Neurosci., February, 2023

Retrieving Conditions from Reference Images for Diffusion Models.
CoRR, 2023

SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos.
CoRR, 2023

Active Perception using Neural Radiance Fields.
CoRR, 2023

TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards.
CoRR, 2023

Design and Evaluation of Motion Planners for Quadrotors.
CoRR, 2023

Analog Content-Addressable Memory from Complementary FeFETs.
CoRR, 2023

Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience.
CoRR, 2023

Taming AI Bots: Controllability of Neural States in Large Language Models.
CoRR, 2023

Learning Capacity: A Measure of the Effective Dimensionality of a Model.
CoRR, 2023

The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold.
CoRR, 2023

Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis.
CoRR, 2023

Mesh Strikes Back: Fast and Efficient Human Reconstruction from RGB videos.
CoRR, 2023

Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Budgeting Counterfactual for Offline RL.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Value of Out-of-Distribution Data.
Proceedings of the International Conference on Machine Learning, 2023

A Picture of the Space of Typical Learnable Tasks.
Proceedings of the International Conference on Machine Learning, 2023

Beyond mAP: Towards Better Evaluation of Instance Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023


2022
Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.
Medical Image Anal., 2022

Data drift correction via time-varying importance weight estimator.
CoRR, 2022

A Model for Perimeter-Defense Problems with Heterogeneous Teams.
CoRR, 2022

MammoDL: Mammographic Breast Density Estimation using Federated Learning.
CoRR, 2022

Machine Learning Models Are Not Necessarily Biased When Constructed Properly: Evidence from Neuroimaging Studies.
CoRR, 2022

Prospective Learning: Back to the Future.
CoRR, 2022

Does the Data Induce Capacity Control in Deep Learning?
Proceedings of the International Conference on Machine Learning, 2022

Deep Reference Priors: What is the best way to pretrain a model?
Proceedings of the International Conference on Machine Learning, 2022

Model Zoo: A Growing Brain That Learns Continually.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Does the Geometry of the Data Control the Geometry of Neural Predictions? (Student Abstract).
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
PennSyn2Real: Training Object Recognition Models Without Human Labeling.
IEEE Robotics Autom. Lett., 2021

A free-energy principle for representation learning.
Mach. Learn. Sci. Technol., 2021

Boosting a Model Zoo for Multi-Task and Continual Learning.
CoRR, 2021

Embracing the Disharmony in Heterogeneous Medical Data.
CoRR, 2021

Continuous Doubly Constrained Batch Reinforcement Learning.
CoRR, 2021

Continuous Doubly Constrained Batch Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Harmonization with Flow-Based Causal Inference.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Scalable Reinforcement Learning Policies for Multi-Agent Control.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Deformable Linear Object Prediction Using Locally Linear Latent Dynamics.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

An Information-Geometric Distance on the Space of Tasks.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data.
IEEE J. Biomed. Health Informatics, 2020

A Geometric Interpretation of Stochastic Gradient Descent Using Diffusion Metrics.
Entropy, 2020

Directional adversarial training for cost sensitive deep learning classification applications.
Eng. Appl. Artif. Intell., 2020

DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning.
CoRR, 2020

BayesRace: Learning to race autonomously using prior experience.
CoRR, 2020

TraDE: Transformers for Density Estimation.
CoRR, 2020

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Proximal Deterministic Policy Gradient.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Rethinking the Hyperparameters for Fine-tuning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Meta-Q-Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

A Baseline for Few-Shot Image Classification.
Proceedings of the 8th International Conference on Learning Representations, 2020

BayesRace: Learning to race autonomously using prior experience.
Proceedings of the 4th Conference on Robot Learning, 2020

2019
P3O: Policy-on Policy-off Policy Optimization.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018
Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks.
Proceedings of the 2018 Information Theory and Applications Workshop, 2018

2017
Deep Relaxation: partial differential equations for optimizing deep neural networks.
CoRR, 2017

Parle: parallelizing stochastic gradient descent.
CoRR, 2017

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys.
Proceedings of the 5th International Conference on Learning Representations, 2017

Partial differential equations for training deep neural networks.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2015
Trivializing The Energy Landscape Of Deep Networks.
CoRR, 2015

2014
Game theoretic controller synthesis for multi-robot motion planning Part I: Trajectory based algorithms.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

Sampling-based algorithms for optimal motion planning using process algebra specifications.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

Incremental minimum-violation control synthesis for robots interacting with external agents.
Proceedings of the American Control Conference, 2014

2013
Incremental sampling-based algorithm for minimum-violation motion planning.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Sampling-based algorithms for continuous-time POMDPs.
Proceedings of the American Control Conference, 2013

2012
Sampling-based algorithm for filtering using Markov chain approximations.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2009
Localization using Average Landmark Vector in the Presence of Clutter.
Proceedings of the World Congress on Nature & Biologically Inspired Computing, 2009


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