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- University of California San DiegoUS25 papers
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- École Polytechnique Fédérale de LausanneCH9 papers
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19 papers · 1 filter
Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning
Jared Levy, Aarti Lalwani, Elijah Wyckoff +4
Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing these movements is important for…
A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation
Jihoon Moon, Debasmit Das, C. S. George Lee
In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem and propose a novel multi-stage framework to solve real-world situations when the target data are…
Differentiable Joint Pruning and Quantization for Hardware Efficiency
Ying Wang, Yadong Lu, Tijmen Blankevoort
We present a differentiable joint pruning and quantization (DJPQ) scheme. We frame neural network compression as a joint gradient-based optimization problem, trading off between mo…
Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel +4
We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition o…
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen +2
When quantizing neural networks, assigning each floating-point weight to its nearest fixed-point value is the predominant approach. We find that, perhaps surprisingly, this is not…
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen +1
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medi…