Search Results for author:
Found 43 papers, 11 papers with code
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
Online Camera-to-ground Calibration for Autonomous Driving
Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner.
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node.
Rotation-Invariant Transformer for Point Cloud Matching
To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task.
Task-Specific Context Decoupling for Object Detection
Classification and localization are two main sub-tasks in object detection.
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition
Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision.
Geometric Transformer for Fast and Robust Point Cloud Registration
Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.
Training Vision Transformers with Only 2040 Images
Vision Transformers (ViTs) is emerging as an alternative to convolutional neural networks (CNNs) for visual recognition.
A Unified Pruning Framework for Vision Transformers
Recently, vision transformer (ViT) and its variants have achieved promising performances in various computer vision tasks.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Mixup Without Hesitation
In this paper, we find that mixup constantly explores the representation space, and inspired by the exploration-exploitation dilemma in reinforcement learning, we propose mixup Without hesitation (mWh), a concise, effective, and easy-to-use training algorithm.
Robust Attacks on Deep Learning Face Recognition in the Physical World
Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems.
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation
Real-time understanding in video is crucial in various AI applications such as autonomous driving.
Dynamic Phase Diagram of an Orthogonal Spin Torque device: Topological Category
The magnetization evolution of the free layer in an orthogonal spin-torque device is studied based on a macrospin model.
Mesoscale and Nanoscale Physics
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
Crude oil price forecasting incorporating news text
Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them.
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
In this paper, we propose a new parallel multi-block stochastic ADMM for distributed stochastic optimization, where each node is only required to perform simple stochastic gradient descent updates.
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
We show that for stochastic non-convex optimization under the P-L condition, the classical data-parallel SGD with exponentially increasing batch sizes can achieve the fastest known $O(1/(NT))$ convergence with linear speedup using only $\log(T)$ communication rounds.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Recent developments on large-scale distributed machine learning applications, e. g., deep neural networks, benefit enormously from the advances in distributed non-convex optimization techniques, e. g., distributed Stochastic Gradient Descent (SGD).
Solving Non-smooth Constrained Programs with Lower Complexity than \mathcal{O}(1/\varepsilon): A Primal-Dual Homotopy Smoothing Approach
In this paper, we show that by leveraging a local error bound condition on the dual function, the proposed algorithm can achieve a better primal convergence time of $\mathcal{O}\l(\varepsilon^{-2/(2+\beta)}\log_2(\varepsilon^{-1})\r)$, where $\beta\in(0, 1]$ is a local error bound parameter.
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Our approach only needs to modify the input image and can work with any network to improve its performance.
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
First, we introduce methods that learn network representations of entities in the knowledge graph capturing these varied aspects of similarity.
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker.
Fast K-Means Clustering with Anderson Acceleration
We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering.
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization
As it requires a huge number of parameters when exposed to high dimensional inputs in video detection and classification, there is a grand challenge to develop a compact yet accurate video comprehension at terminal devices.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications.
Online Convex Optimization with Stochastic Constraints
This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i. i. d.
S-OHEM: Stratified Online Hard Example Mining for Object Detection
S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP).
A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and $O(1)$ Constraint Violations for Online Convex Optimization with Long Term Constraints
That prior work proposes an algorithm to achieve $O(\sqrt{T})$ regret and $O(T^{3/4})$ constraint violations for general problems and another algorithm to achieve an $O(T^{2/3})$ bound for both regret and constraint violations when the constraint set can be described by a finite number of linear constraints.
