Search Results for author:
Found 23 papers, 13 papers with code
FusionINN: Invertible Image Fusion for Brain Tumor Monitoring
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image.
Quantile-based Maximum Likelihood Training for Outlier Detection
Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption.
Examining Computational Performance of Unsupervised Concept Drift Detection: A Survey and Beyond
We show that the previous works consider computational performance only as a secondary objective and do not have a benchmark for such evaluation.
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians.
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
TransDrift: Modeling Word-Embedding Drift using Transformer
In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines.
Scalable Robust Federated Learning with Provable Security Guarantees
Federated averaging, the most popular aggregation approach in federated learning, is known to be vulnerable to failures and adversarial updates from clients that wish to disrupt training.
InFlow: Robust outlier detection utilizing Normalizing Flows
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
Bulk Modulus along Jamming Transition Lines of Bidisperse Granular Packings
We find that the bulk modulus $K$ jumps at $X^{*}_{\mathrm S}(\delta = 0. 15) \approx 0. 21$, at the maximum jamming density, where both particle species mix most efficiently, while for $X_{\mathrm S} < X^{*}_{\mathrm S}$ $K$ is decoupled in two scenarios as a result of the first and second jamming transition.
Soft Condensed Matter
Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey
In this survey, we present the current state of deep learning applications in the tasks related to detection, prediction, and alleviation of congestion.
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
This paper considers the case where there is a single, powerful, \emph{central agent} that can observe the entire observation space, and there are multiple, low-powered \emph{local agents} that can only receive local observations and are not able to communicate with each other.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available.
CrypTFlow2: Practical 2-Party Secure Inference
We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation.
Activity-based contact network scaling and epidemic propagation in metropolitan areas
Given the growth of urbanization and emerging pandemic threats, more sophisticated models are required to understand disease propagation and investigate the impacts of intervention strategies across various city types.
Physics and Society Populations and Evolution
Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas
The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation.
CrypTFlow: Secure TensorFlow Inference
Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security.
Structural Similarity based Anatomical and Functional Brain Imaging Fusion
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image.
HAUAR: Home Automation Using Action Recognition
Today, many of the home automation systems deployed are mostly controlled by humans.
U-PC: Unsupervised Planogram Compliance
We present an end-to-end solution for recognizing merchandise displayed in the shelves of a supermarket.
Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge
This paper describes our approach to the Bosch production line performance challenge run by Kaggle. com.
