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Found 43 papers, 17 papers with code
Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces
Convergence analysis of Markov chain Monte Carlo methods in high-dimensional statistical applications is increasingly recognized.
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system.
Soft-constrained Schrodinger Bridge: a Stochastic Control Approach
Schr\"{o}dinger bridge can be viewed as a continuous-time stochastic control problem where the goal is to find an optimally controlled diffusion process with a pre-specified terminal distribution $\mu_T$.
Joint Problems in Learning Multiple Dynamical Systems
Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory.
Group-blind optimal transport to group parity and its constrained variants
Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by sensitive attributes, e. g., gender, race.
VCD: A Video Conferencing Dataset for Video Compression
We present the Video Conferencing Dataset (VCD) for evaluating video codecs for real-time communication, the first such dataset focused on video conferencing.
Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning
The evolution of EMS from HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotal shift.
Importance is Important: A Guide to Informed Importance Tempering Methods
Informed importance tempering (IIT) is an easy-to-implement MCMC algorithm that can be seen as an extension of the familiar Metropolis-Hastings algorithm with the special feature that informed proposals are always accepted, and which was shown in Zhou and Smith (2022) to converge much more quickly in some common circumstances.
Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning
The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multipleoutput (MIMO) control.
CoRTX: Contrastive Framework for Real-time Explanation
In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.
Short-term and long-term memory self-attention network for segmentation of tumours in 3D medical images
Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes.
Rate-Perception Optimized Preprocessing for Video Coding
In the past decades, lots of progress have been done in the video compression field including traditional video codec and learning-based video codec.
Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning
The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function.
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection.
Ranked #13 on
Object Detection
on PASCAL VOC 2007
Fairness in Forecasting of Observations of Linear Dynamical Systems
We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably.
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning
The study suggested that the MADRL with an independence ratio of 0. 2 is the best, and more than 2. 4% of energy can be saved over the conventional DRL framework.
Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions
In a closed-loop view of the AI system and its users, the equal treatment concerns one pass through the loop.
MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment
In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling.
Ranked #1 on
Point Cloud Quality Assessment
on SJTU-PCQA
Accelerating Shapley Explanation via Contributive Cooperator Selection
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
Deep Neural Network for Blind Visual Quality Assessment of 4K Content
In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality.
DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary
Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years.
DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention
Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices.
Apache Submarine: A Unified Machine Learning Platform Made Simple
As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity.
FPB: Feature Pyramid Branch for Person Re-Identification
In this paper, we propose a lightweight Feature Pyramid Branch (FPB) to extract features from different layers of networks and aggregate them in a bidirectional pyramid structure.
Ranked #6 on
Person Re-Identification
on CUHK03 labeled
Subgroup Fairness in Two-Sided Markets
In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem.
BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learning
We apply the traditional DNN and our BubbleNet to train the coarsened simulation data and predict the physics fields of both the two bubbly flow cases.
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations.
Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes
A series of high-dimensional consistency results is obtained, including the strong selection consistency of an empirical Bayes model for structure learning.
Statistics Theory Computation Statistics Theory 62F15, 62J05
Fairness in Forecasting and Learning Linear Dynamical Systems
In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population.
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
We design a regularisation technique to regulate the domain adaptation.
BiCANet: Bi-directional Contextual Aggregating Network for Image Semantic Segmentation
Exploring contextual information in convolution neural networks (CNNs) has gained substantial attention in recent years for semantic segmentation.
Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification
Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset.
Entropy Minimization vs. Diversity Maximization for Domain Adaptation
Entropy minimization has been widely used in unsupervised domain adaptation (UDA).
Ranked #6 on
Domain Adaptation
on ImageCLEF-DA
Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS.
FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation
The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.
ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation
The whole network has nearly symmetric architecture, which is mainly composed of a series of factorized convolution unit (FCU) and its parallel counterparts (PFCU).
Ranked #28 on
Real-Time Semantic Segmentation
on Cityscapes test
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #29 on
Real-Time Semantic Segmentation
on Cityscapes test
A lossless data hiding scheme in JPEG images with segment coding
In this paper, we propose a lossless data hiding scheme in JPEG images.
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks.
Ranked #5 on
Image Classification
on smallNORB
Multi-level Chaotic Maps for 3D Textured Model Encryption
In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures.
Multiple types of topological fermions in transition metal silicides
Exotic massless fermionic excitations with non-zero Berry flux, other than Dirac and Weyl fermions, could exist in condensed matter systems under the protection of crystalline symmetries, such as spin-1 excitations with 3-fold degeneracy and spin-3/2 Rarita-Schwinger-Weyl fermions.
Materials Science Mesoscale and Nanoscale Physics Applied Physics
Fiscal stimulus as an optimal control problem
During the Great Recession, Democrats in the United States argued that government spending could be utilized to "grease the wheels" of the economy in order to create wealth and to increase employment; Republicans, on the other hand, contended that government spending is wasteful and discouraged investment, thereby increasing unemployment.
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Our second contribution is to derive a practical algorithm based on this reduction.
