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- Nitish V. ThakorSuggested from co-authorship
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Works33 from public data
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- A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors18
This paper develops a technique for generalized motion segmentation based on spatial statistics across time frames and introduces the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it.
- Neuromorphic vision and tactile fusion for upper limb prosthesis control15
It is discovered that combining both visual and tactile information in a real-time closed loop feedback strategy generally decreased the completion time of a task involving picking up and manipulating objects compared to using a single modality for feedback.
- Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity14
A novel, model-based spatiotemporal correlation matching method to estimate the orientation of the boundary edge while a piezoresistive tactile sensor array attached to robotic arm palpates over the object.
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- A prototype of automated child monitoring system6
The proposed setup is a low cost surveillance system and can be implemented at home or childcare facilities, using Raspberry Pi microcomputer and a camera, which is made dynamic using Passive Infrared (PIR) sensors and Servo motor.
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- Investigating Convolutional Neural Networks using Spatial Orderness5
A statistical metric called spatial orderness is proposed, which quantifies the extent to which the input data (2D) obeys the underlying spatial ordering at various scales, which mainly finds that adding convolutional layers to a CNN could be counterproductive for data bereft of spatial order at higher scales.
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- Exploring Local Transformation Shared Weights in Convolutional Neural Networks2
This work focuses on two conjugate methods in literature which increase the degree of weight-sharing within CNNs, showcasing opposing philosophies, and finds that for small test-data distortions, TSKE outperforms TSMP, whereas for large test- data distortions,TSMP showcases superior performance.
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- Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks1
A theoretical justification for the surprising effect of LR schedules is provided and a proposed LR schedule for network pruning called SILO, which stands for S-shaped Improved Learning rate Optimization is proposed, resulting in performance competitive with the Oracle with significantly lower complexity.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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