Search Results for author: Aditya Balu

Found 28 papers, 7 papers with code

Latent Diffusion Models for Structural Component Design

no code implementations20 Sep 2023 Ethan Herron, Jaydeep Rade, Anushrut Jignasu, Baskar Ganapathysubramanian, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy

Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions.

Image Generation

ZeroForge: Feedforward Text-to-Shape Without 3D Supervision

1 code implementation14 Jun 2023 Kelly O. Marshall, Minh Pham, Ameya Joshi, Anushrut Jignasu, Aditya Balu, Adarsh Krishnamurthy, Chinmay Hegde

Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations.

Text-to-Shape Generation

Out-of-distribution detection algorithms for robust insect classification

no code implementations2 May 2023 Mojdeh Saadati, Aditya Balu, Shivani Chiranjeevi, Talukder Zaki Jubery, Asheesh K Singh, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramanian

One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images that are wildly out of the distribution (e. g., images like vehicles, animals, humans, or a blurred image of an insect or insect class that is not yet trained on) can produce an incorrect insect classification.

Classification Out-of-Distribution Detection +1

SpecXAI -- Spectral interpretability of Deep Learning Models

no code implementations20 Feb 2023 Stefan Druc, Peter Wooldridge, Adarsh Krishnamurthy, Soumik Sarkar, Aditya Balu

Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models.

Explainable Artificial Intelligence (XAI)

Neural PDE Solvers for Irregular Domains

no code implementations7 Nov 2022 Biswajit Khara, Ethan Herron, Zhanhong Jiang, Aditya Balu, Chih-Hsuan Yang, Kumar Saurabh, Anushrut Jignasu, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.

Distributed Online Non-convex Optimization with Composite Regret

no code implementations21 Sep 2022 Zhanhong Jiang, Aditya Balu, Xian Yeow Lee, Young M. Lee, Chinmay Hegde, Soumik Sarkar

To address these two issues, we propose a novel composite regret with a new network regret-based metric to evaluate distributed online optimization algorithms.

Concept Activation Vectors for Generating User-Defined 3D Shapes

no code implementations29 Apr 2022 Stefan Druc, Aditya Balu, Peter Wooldridge, Adarsh Krishnamurthy, Soumik Sarkar

We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD).

MDPGT: Momentum-based Decentralized Policy Gradient Tracking

1 code implementation6 Dec 2021 Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu, Young M. Lee, Chinmay Hegde, Soumik Sarkar

We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.

Multi-agent Reinforcement Learning Policy Gradient Methods +3

NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs

no code implementations4 Oct 2021 Biswajit Khara, Aditya Balu, Ameya Joshi, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs).

Differentiable Spline Approximations

no code implementations NeurIPS 2021 Minsu Cho, Aditya Balu, Ameya Joshi, Anjana Deva Prasad, Biswajit Khara, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, Chinmay Hegde

Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.

3D Point Cloud Reconstruction BIG-bench Machine Learning +3

NURBS-Diff: A Differentiable Programming Module for NURBS

no code implementations29 Apr 2021 Anjana Deva Prasad, Aditya Balu, Harshil Shah, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy

These derivatives are used to define an approximate Jacobian used for performing the "backward" evaluation to train the deep learning models.

BIG-bench Machine Learning Point cloud reconstruction

Cross-Gradient Aggregation for Decentralized Learning from Non-IID data

1 code implementation2 Mar 2021 Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar

Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i. e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP).

Continual Learning

Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization

no code implementations9 Dec 2020 Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy

We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm.

BIG-bench Machine Learning

Decentralized Deep Learning using Momentum-Accelerated Consensus

no code implementations21 Oct 2020 Aditya Balu, Zhanhong Jiang, Sin Yong Tan, Chinmay Hedge, Young M Lee, Soumik Sarkar

In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server).

A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning

1 code implementation18 Oct 2019 Yasaman Esfandiari, Aditya Balu, Keivan Ebrahimi, Umesh Vaidya, Nicola Elia, Soumik Sarkar

Under the assumptions that the cost function is convex and uncertainties enter concavely in the robust learning problem, we analytically show that our algorithm converges asymptotically to the robust optimal solution under a general adversarial budget constraints as induced by $\ell_p$ norm, for $1\leq p\leq \infty$.

On Higher-order Moments in Adam

no code implementations15 Oct 2019 Zhanhong Jiang, Aditya Balu, Sin Yong Tan, Young M. Lee, Chinmay Hegde, Soumik Sarkar

In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments.

Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning

no code implementations29 Nov 2018 Xian Yeow Lee, Aditya Balu, Daniel Stoecklein, Baskar Ganapathysubramanian, Soumik Sarkar

A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one.

reinforcement-learning Reinforcement Learning (RL)

3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys

no code implementations23 Nov 2018 Rahul Singh, Aayush Sharma, Onur Rauf Bingol, Aditya Balu, Ganesh Balasubramanian, Duane D. Johnson, Soumik Sarkar

In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties.

On Consensus-Optimality Trade-offs in Collaborative Deep Learning

no code implementations30 May 2018 Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar

In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality.


Multi-level 3D CNN for Learning Multi-scale Spatial Features

1 code implementation30 May 2018 Sambit Ghadai, Xian Lee, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy

The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.

3D Object Recognition

Collaborative Deep Learning in Fixed Topology Networks

no code implementations NeurIPS 2017 Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar

There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes.

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