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Found 30 papers, 10 papers with code
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
Generative Fractional Diffusion Models
We generalize the continuous time framework for score-based generative models from an underlying Brownian motion (BM) to an approximation of fractional Brownian motion (FBM).
mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed.
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner.
Optimizing Vision Transformers for Medical Image Segmentation
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an $\mathrm{SO}^{+}(2, 1)$-equivariant neural network.
The Fully Convolutional Transformer for Medical Image Segmentation
To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs.
Ranked #1 on
Medical Image Segmentation
on ACDC
Improving Sequential Query Recommendation with Immediate User Feedback
We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework.
Rotation Equivariant 3D Hand Mesh Generation from a Single RGB Image
Our rotation equivariant model outperforms state-of-the-art methods on a real-world dataset and we demonstrate that it accurately captures the shape and pose in the generated meshes under rotation of the input hand.
Subgraph Permutation Equivariant Networks
In this work we develop a new method, named Sub-graph Permutation Equivariant Networks (SPEN), which provides a framework for building graph neural networks that operate on sub-graphs, while using a base update function that is permutation equivariant, that are equivariant to a novel choice of automorphism group.
CpT: Convolutional Point Transformer for 3D Point Cloud Processing
It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods.
Learning Robust Controllers Via Probabilistic Model-Based Policy Search
Model-based Reinforcement Learning estimates the true environment through a world model in order to approximate the optimal policy.
Model-based Reinforcement Learning
reinforcement-learning
+1
Rotation Equivariant Deforestation Segmentation and Driver Classification
Deforestation has become a significant contributing factor to climate change and, due to this, both classifying the drivers and predicting segmentation maps of deforestation has attracted significant interest.
Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose
We infer finger 3D position $(x, y, z)$ and pose (pitch and yaw) on a mobile device using capacitive sensors which can sense the finger up to 5cm above the screen.
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations
We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms.
IntenT5: Search Result Diversification using Causal Language Models
Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents.
Adversarial learning of cancer tissue representations
We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations.
Survey: Leakage and Privacy at Inference Time
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.
A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs
Finally, we demonstrate that the model is interpretable by generating molecules controlled by molecular properties, and we then analyse and visualise the learned latent representation.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
We propose a new probabilistic method for unsupervised recovery of corrupted data.
Learning a low dimensional manifold of real cancer tissue with PathologyGAN
We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space.
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
Spatial images from temporal data
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light).
Penalizing small errors using an Adaptive Logarithmic Loss
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year.
PathologyGAN: Learning deep representations of cancer tissue
We show that our model generates high quality images, with a FID of 16. 65 (breast cancer) and 32. 05 (colorectal cancer).
Variational Sparse Coding
We show that these sparse representations are advantageous over standard VAE representations on two benchmark classification tasks (MNIST and Fashion-MNIST) by demonstrating improved classification accuracy and significantly increased robustness to the number of latent dimensions.
Variational Inference for Computational Imaging Inverse Problems
In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts.
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre.
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Light scattered from multiple surfaces can be used to retrieve information of hidden environments.
