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Found 15 papers, 6 papers with code
Interactions Across Blocks in Post-Training Quantization of Large Language Models
Typically, individual substructures, such as layers or blocks of layers, are quantized with the objective of minimizing quantization errors in their pre-activations by fine-tuning the corresponding weights.
How to Choose a Reinforcement-Learning Algorithm
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems.
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
The found radiofrequency pulse train designs generate an optimal signal for the NN to perform the SR task.
Scale-Equivariant Deep Learning for 3D Data
The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional operation.
Scene Graph Generation for Better Image Captioning?
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language.
Rotation-Equivariant Deep Learning for Diffusion MRI
So far, they have been proposed for 2D and 3D data.
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions
Computer-aided drug discovery is an essential component of modern drug development.
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods
Convolutional networks are successful due to their equivariance/invariance under translations.
Learning to Evolve
Here we show that learning to evolve, i. e. learning to mutate and recombine better than at random, improves the result of evolution in terms of fitness increase per generation and even in terms of attainable fitness.
q-Space Novelty Detection with Variational Autoencoders
Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output.
Clustering with Deep Learning: Taxonomy and New Methods
In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks.
Regularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other.
3D Deep Learning for Biological Function Prediction from Physical Fields
In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.
Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images
A contact map is a compact representation of the three-dimensional structure of a protein via the pairwise contacts between the amino acid constituting the protein.
FlowNet: Learning Optical Flow with Convolutional Networks
Optical flow estimation has not been among the tasks where CNNs were successful.
