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Found 24 papers, 12 papers with code
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input.
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis
Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models.
Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis
Any-scale image synthesis offers an efficient and scalable solution to synthesize photo-realistic images at any scale, even going beyond 2K resolution.
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo).
Marginal-Certainty-aware Fair Ranking Algorithm
In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility.
Wavelet Diffusion Models are fast and scalable Image Generators
Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances.
Ranked #1 on
Image Generation
on CelebA-HQ 512x512
Monotonic Gaussian process for physics-constrained machine learning with materials science applications
Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets.
Reinforcement Learning to Rank with Coarse-grained Labels
The results on simulated coarse-grained labeled dataset show that while using coarse-grained labels to train an RL model for LTR tasks still can not outperform traditional approaches using fine-grained labels, it still achieve somewhat promising results and is potentially helpful for future research in LTR.
Learning to Rank Rationales for Explainable Recommendation
Seeing this gap, we propose a model named Semantic-Enhanced Bayesian Personalized Explanation Ranking (SE-BPER) to effectively combine the interaction information and semantic information.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e. g., learning domain-invariant representations and its trade-off.
Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers
As a result, the proposed Scalable$^3$-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC.
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox
Our toolbox is an important resource for researchers to conduct experiments on ULTR algorithms with different configurations as well as testing their own algorithms with the supported features.
Progressive Semantic Segmentation
In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels.
Ranked #4 on
Land Cover Classification
on DeepGlobe
Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles.
Ranked #1 on
Facial Makeup Transfer
on CPM-Synt-2
Explore Image Deblurring via Blur Kernel Space
This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
Blind Image Deblurring
Facial Expression Recognition (FER)
+1
FineNet: Frame Interpolation and Enhancement for Face Video Deblurring
The objective of this work is to deblur face videos.
WaNet -- Imperceptible Warping-based Backdoor Attack
With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years.
Input-Aware Dynamic Backdoor Attack
In recent years, neural backdoor attack has been considered to be a potential security threat to deep learning systems.
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications
Finally, we couple the third GP along with the classical BO framework to promote the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function.
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture
First, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated massively parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime.
Action Recognition in the Frequency Domain
In this paper, we describe a simple strategy for mitigating variability in temporal data series by shifting focus onto long-term, frequency domain features that are less susceptible to variability.
