Search Results for author:
Found 20 papers, 3 papers with code
Contextual Object Detection with Multimodal Large Language Models
Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction.
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
We show that when there is no representation error and the sensing vectors are Gaussian, roughly $O(k \log L)$ samples suffice to ensure that a PGD algorithm converges linearly to a point achieving the optimal statistical rate using arbitrary initialization.
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research.
Generative Principal Component Analysis
We perform experiments on various image datasets for spiked matrix and phase retrieval models, and illustrate performance gains of our method to the classic power method and the truncated power method devised for sparse principal component analysis.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the vector.
TS4Net: Two-Stage Sample Selective Strategy for Rotating Object Detection
At present, most of the rotating object detection datasets focus on the field of remote sensing, and these images are usually shot in high-altitude scenes.
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
Disentangled Recurrent Wasserstein Autoencoder
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework.
VPQC: A Domain-Specific Vector Processor for Post-Quantum Cryptography Based on RISC-V Architecture
In the 5G era, massive devices need to be securely connected to the edge of communication networks, while emerging quantum computers can easily crack the traditional public-key ciphers.
Hardware Architecture
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
Scalable Approximate Inference and Some Applications
Approximate inference in probability models is a fundamental task in machine learning.
Stein Variational Inference for Discrete Distributions
In addition, such transform can be straightforwardly employed in gradient-free kernelized Stein discrepancy to perform goodness-of-fit (GOF) test on discrete distributions.
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones.
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data
Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar.
Deep Generative Video Compression
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy.
Deep Probabilistic Video Compression
We propose a variational inference approach to deep probabilistic video compression.
Stein Variational Gradient Descent Without Gradient
Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions.
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals.
Stein Variational Adaptive Importance Sampling
We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS).
Bootstrap Model Aggregation for Distributed Statistical Learning
In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation.
