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Found 6 papers, 4 papers with code
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
Dynamic Precision Analog Computing for Neural Networks
We propose extending analog computing architectures to support varying levels of precision by repeating operations and averaging the result, decreasing the impact of noise.
Confounding Tradeoffs for Neural Network Quantization
Many neural network quantization techniques have been developed to decrease the computational and memory footprint of deep learning.
Counterfactual Fairness in Text Classification through Robustness
In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different?
Szloca: towards a framework for full 3D tracking through a single camera in context of interactive arts
Realtime virtual data of objects and human presence in a large area holds a valuable key in enabling many experiences and applications in various industries and with exponential rise in the technological development of artificial intelligence, computer vision has expanded the possibilities of tracking and classifying things through just video inputs, which is also surpassing the limitations of most popular and common hardware setups known traditionally to detect human pose and position, such as low field of view and limited tracking capacity.
