Zobrazeno 1 - 10
of 621
pro vyhledávání: '"TRIVEDI, AMIT"'
We introduce a precision polarization scheme for DNN inference that utilizes only very low and very high precision levels, assigning low precision to the majority of network weights and activations while reserving high precision paths for targeted er
Externí odkaz:
http://arxiv.org/abs/2411.05845
Autor:
Darabi, Nastaran, Jayasuriya, Dinithi, Naik, Devashri, Tulabandhula, Theja, Trivedi, Amit Ranjan
Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack
Externí odkaz:
http://arxiv.org/abs/2409.12379
Autor:
Hashem, Maeesha Binte, Parpillon, Benjamin, Kumar, Divake, Jayasuria, Dinithi, Trivedi, Amit Ranjan
In this work, we propose "TimeFloats," an efficient train-in-memory architecture that performs 8-bit floating-point scalar product operations in the time domain. While building on the compute-in-memory paradigm's integrated storage and inferential co
Externí odkaz:
http://arxiv.org/abs/2409.00495
In this work, we propose a disruptively frugal LiDAR perception dataflow that generates rather than senses parts of the environment that are either predictable based on the extensive training of the environment or have limited consequence to the over
Externí odkaz:
http://arxiv.org/abs/2406.07833
We present a novel statistical approach to incorporating uncertainty awareness in model-free distributional reinforcement learning involving quantile regression-based deep Q networks. The proposed algorithm, $\textit{Calibrated Evidential Quantile Re
Externí odkaz:
http://arxiv.org/abs/2402.07107
Autor:
Darabi, Nastaran, Shukla, Priyesh, Jayasuriya, Dinithi, Kumar, Divake, Stutts, Alex C., Trivedi, Amit Ranjan
This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial intelligence i
Externí odkaz:
http://arxiv.org/abs/2401.17481
Autor:
Darabi, Nastaran, Trivedi, Amit R.
Edge computing is a promising solution for handling high-dimensional, multispectral analog data from sensors and IoT devices for applications such as autonomous drones. However, edge devices' limited storage and computing resources make it challengin
Externí odkaz:
http://arxiv.org/abs/2309.11048
Autor:
Parente, Domenico, Darabi, Nastaran, Stutts, Alex C., Tulabandhula, Theja, Trivedi, Amit Ranjan
This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor. We specifically discuss its application for visual odometry (
Externí odkaz:
http://arxiv.org/abs/2309.11018
Autor:
Darabi, Nastaran, Tayebati, Sina, S., Sureshkumar, Ravi, Sathya, Tulabandhula, Theja, Trivedi, Amit R.
Complex sensors such as LiDAR, RADAR, and event cameras have proliferated in autonomous robotics to enhance perception and understanding of the environment. Meanwhile, these sensors are also vulnerable to diverse failure mechanisms that can intricate
Externí odkaz:
http://arxiv.org/abs/2309.11006
In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance. Three-dimensional (3D) object detection, a critical robotics operation, has seen significant advancements; however, the major
Externí odkaz:
http://arxiv.org/abs/2309.09593