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Found 13 papers, 3 papers with code
Towards Stable Preferences for Stakeholder-aligned Machine Learning
In response to the pressing challenge of kidney allocation, characterized by growing demands for organs, this research sets out to develop a data-driven solution to this problem, which also incorporates stakeholder values.
Learning Permutation Invariant Representations using Memory Networks
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances.
Convolutional Neural Networks Regularized by Correlated Noise
Neurons in the visual cortex are correlated in their variability.
Quantitatively Evaluating GANs With Divergences Proposed for Training
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
BitNet: Bit-Regularized Deep Neural Networks
The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values.
Generative Adversarial Parallelization
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation.
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition.
Automatic Moth Detection from Trap Images for Pest Management
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems.
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input.
Learning Human Identity from Motion Patterns
We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors.
Learning with hidden variables
Learning and inferring features that generate sensory input is a task continuously performed by cortex.
Generative Class-conditional Autoencoders
Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain.
Theano-based Large-Scale Visual Recognition with Multiple GPUs
In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs.
