Search Results for author:
Found 39 papers, 10 papers with code
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Then we design a novel omnidirectional attention mechanism in OCPM to capture the context information in the permutation.
Energy-Inspired Self-Supervised Pretraining for Vision Models
In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e. g., an extra decoder.
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization
HMOE uses hypernetworks taking vectors as input to generate experts' weights, which allows experts to share useful meta-knowledge and enables exploring experts' similarities in a low-dimensional vector space.
Motion correction in MRI using deep learning and a novel hybrid loss function
Evaluation used simulated T1 and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans.
LF-SLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents
We collect the PALVIO dataset using a Panoramic Annular Lens (PAL) system with an entire FoV of 360\deg x(40\deg-120\deg) and IMU sensor to address the lack of panoramic SLAM datasets.
Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning
The proposed method significantly reduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBF without sacrificing the SNR.
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning
Guided by that maximum rate, a novel and efficient hierarchical network pruning algorithm is developed to maximally condense the neuronal network structure without sacrificing network performance.
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
Although VCG-based multi-slot auctions (e. g., VCG, WVCG) make it theoretically possible to model global externalities (e. g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare.
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot.
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks
With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e. g., e-commerce and news feed sites).
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem.
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding
PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks.
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV.
Learning to Learn Dense Gaussian Processes for Few-Shot Learning
In contrast to sparse Gaussian processes, we define a set of dense inducing variables to be of a much larger size than the support set in each task, which collects prior knowledge from experienced tasks.
Image Generation using Continuous Filter Atoms
In this paper, we model the subspace of convolutional filters with a neural ordinary differential equation (ODE) to enable gradual changes in generated images.
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks
In this paper, we introduce spatiotemporal joint filter decomposition to decouple spatial and temporal learning, while preserving spatiotemporal dependency in a video.
Continual Learning with Filter Atom Swapping
In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms.
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding
We introduce Meta-OLE, a new geometry-regularized method for fast adaptation to novel tasks in few-shot image classification.
Fast Adaptive Anomaly Detection
Anything outside of a given normal population is by definition an anomaly.
A Joint Subspace View to Convolutional Neural Networks
In other words, a CNN is now reduced to layers of filter atoms, typically a few hundred of parameters per layer, with a common block of subspace coefficients shared across layers.
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed
Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments.
Cross-time functional connectivity analysis
A large body of literature has shown the substantial inter-regional functional connectivity in the mammal brain.
Resting state fMRI-based temporal coherence mapping
A few TCM properties were collected to measure LRTC, including the averaged correlation, anti-correlation, the ratio of correlation and anticorrelation, the mean coherent and incoherent duration, and the ratio between the coherent and incoherent time.
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Applying feature dependent network weights have been proved to be effective in many fields.
Cirrus: A Long-range Bi-pattern LiDAR Dataset
Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR.
Using Text to Teach Image Retrieval
Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space.
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution
We then explicitly regularize CNN kernels by enforcing decomposed coefficients to be shared across sub-structures, while leaving each sub-structure only its own dictionary atoms, a few hundreds of parameters typically, which leads to dramatic model reductions.
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images
The results showed that the learning-from-noise strategy produced better output quality than ASLDN trained with relatively high SNR reference.
Range Adaptation for 3D Object Detection in LiDAR
To the best of our knowledge, this paper is the first attempt to study cross-range LiDAR adaptation for object detection in point clouds.
Domain-invariant Learning using Adaptive Filter Decomposition
Domain shifts are frequently encountered in real-world scenarios.
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN).
Stochastic Conditional Generative Networks with Basis Decomposition
One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space.
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data.
In Defense of Single-column Networks for Crowd Counting
Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis.
The Structure Transfer Machine Theory and Applications
Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown.
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning
Arterial spin labeling perfusion MRI is a noninvasive technique for measuring quantitative cerebral blood flow (CBF), but the measurement is subject to a low signal-to-noise-ratio(SNR).
MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting
Based on an empirical analysis of properties of the distance function of the acquired MRF signal and the pre-defined MRF dictionary entries, we proposed a parameter separable MRF DGS method, which breaks the multiplicative computation complexity into an additive one and enabling a resolution scalable multi-resolution DGS process, which was dubbed as MRF ZOOM.
