32 citations · 54 across the 11 of their papers we have counts for
12 papers
SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation
Kaivalya Agrawal, Md Ashiqur Rahman, Raymond A. Yeh +1
Vision-Language-Action (VLA) models are a class of generalist robot policies that map camera images and language instructions directly to robot actions. While promising, these mode…
Tunable Soft Equivariance with Guarantees
Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang +2
Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling…
Group Downsampling with Equivariant Anti-aliasing
Md Ashiqur Rahman, Raymond A. Yeh
Downsampling layers are crucial building blocks in CNN architectures, which help to increase the receptive field for learning high-level features and reduce the amount of memory/co…
HessianForge: Scalable LiDAR reconstruction with Physics-Informed Neural Representation and Smoothness Energy Constraints
Hrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin +2
Accurate and efficient 3D mapping of large-scale outdoor environments from LiDAR measurements is a fundamental challenge in robotics, particularly towards ensuring smooth and artif…
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy +9
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions…
Truly Scale-Equivariant Deep Nets with Fourier Layers
Md Ashiqur Rahman, Raymond A. Yeh
In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance. Rece…
Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
Hrishikesh Viswanath, Md Ashiqur Rahman, Abhijeet Vyas +5
Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving func…
PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators
Md Ashiqur Rahman, Jasorsi Ghosh, Hrishikesh Viswanath +2
We address the problem of generating 3D human motions in dyadic activities. In contrast to the concurrent works, which mainly focus on generating the motion of a single actor from…
AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation
Hrishikesh Viswanath, Md Ashiqur Rahman, Rashmi Bhaskara +1
We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods…
Generative Adversarial Neural Operators
Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar +2
We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences a…
U-NO: U-shaped Neural Operators
Md Ashiqur Rahman, Zachary E. Ross, Kamyar Azizzadenesheli
Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel…
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Akm Ashiquzzaman, Abdul Kawsar Tushar, Md Ashiqur Rahman
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a…