Search Results for author:
Found 21 papers, 8 papers with code
MAGVLT: Masked Generative Vision-and-Language Transformer
Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderate-sized model (fewer than 500M parameters) even without the use of monomodal data and networks.
RMSim: Controlled Respiratory Motion Simulation on Static Patient Scans
The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation.
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
In order to resolve these issues, in this paper, we propose a learnable hash-based episodic count, which we name LECO, that efficiently performs as a task-specific intrinsic reward in hard exploration problems.
Core-shell enhanced single particle model for lithium iron phosphate batteries: model formulation and analysis of numerical solutions
In this paper, a core-shell enhanced single particle model for iron-phosphate battery cells is formulated, implemented, and verified.
Core-shell enhanced single particle model for LiFePO$_4$ batteries
In this paper, a novel electrochemical model for LiFePO$_4$ battery cells that accounts for the positive particle lithium intercalation and deintercalation dynamics is proposed.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Automated Learning Rate Scheduler for Large-batch Training
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning.
Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy
Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment.
DeepPRO: Deep Partial Point Cloud Registration of Objects
The point cloud is partial as it is obtained by a depth sensor capturing only the visible part of the object from a certain viewpoint.
Learning Augmentation Network via Influence Functions
This paper considers an influence function that predicts how generalization performance, in terms of validation loss, is affected by a particular augmented training sample.
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning
Entropy augmented to reward is known to soften the greedy argmax policy to softmax policy.
A Variational Bayes Approach to Adaptive Radio Tomography
Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks.
Learning to Augment Influential Data
The differentiable augmentation model and reformulation of the influence function allow the parameters of the augmented model to be directly updated by backpropagation to minimize the validation loss.
Inserting Videos into Videos
To synthesize a realistic video, the network renders each frame based on the current input and previous frames.
Context-Aware Synthesis and Placement of Object Instances
Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem.
Learning Instance-Aware Object Detection Using Determinantal Point Processes
Recent object detectors find instances while categorizing candidate regions.
Adaptive Bayesian Radio Tomography
Key to success of RTI is to model accurately the shadowing effects as the bi-dimensional integral of the SLF scaled by a weight function, which is estimated using regularized regression.
Signal Processing Applications
Generative Single Image Reflection Separation
Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation.
Unsupervised Holistic Image Generation from Key Local Patches
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior.
Fast and Accurate Head Pose Estimation via Random Projection Forests
In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image.
Face Alignment Using Cascade Gaussian Process Regression Trees
Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization.
