Search Results for author:
Found 82 papers, 19 papers with code
Gender Bias in Natural Language Processing Across Human Languages
In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages.
Roadblocks in Gender Bias Measurement for Diachronic Corpora
The use of word embeddings is an important NLP technique for extracting meaningful conclusions from corpora of human text.
Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products.
Image Classification with Deep Reinforcement Active Learning
Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, where only a small fraction is iteratively selected from a large pool of unlabeled data, and annotated by an expert (a. k. a oracle), and eventually used to update the learning models.
Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance.
GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting
Consequently, we propose GSRender, which naturally employs 3D Gaussian Splatting for occupancy prediction, simplifying the sampling process.
Constraint Learning for Parametric Point Cloud
Stage 1 extracts constraints from B-Rep data or point cloud.
Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms, experimental results confirm that the importance information of different samples and the dual relationship between sample and feature are beneficial for achieving better feature selection.
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
To resolve this, we introduce the Logistic Schedule, a novel noise schedule designed to eliminate singularities, improve inversion stability, and provide a better noise space for image editing.
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models
Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models.
Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models
Moreover, transfer learning experiments reveal that with minimal target domain data for fine-tuning, the model outperforms SOTA methods trained on full target domain data.
Generative AI Application for Building Industry
Additionally, the study considers the broader implications of AI integration, including the development of AI-powered tools for comprehensive code compliance across various regulatory domains and the potential for AI to revolutionize workforce training through realistic simulations.
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery
Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD).
Training-Free Point Cloud Recognition Based on Geometric and Semantic Information Fusion
For the geometric branch, we adopt a non-parametric strategy to extract geometric features.
Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-resolution Sensing and Communication
We compare the performance of the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training.
Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models
Knowledge-based Visual Question Answering (KVQA) requires both image and world knowledge to answer questions.
Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference
Meanwhile, most existing class incremental learning approaches are unsuitable for rPPG measurement.
Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization
Furthermore, if the local cost functions are strongly convex, then all nodes' states converge to the minimizer of the global cost function and the auxiliary states in the stochastic gradient tracking algorithm converge to the gradient value of the global cost function at the minimizer uniformly in mean square.
Panoptic-FlashOcc: An Efficient Baseline to Marry Semantic Occupancy with Panoptic via Instance Center
On the Occ3D-nuScenes benchmark, it achieves exceptional performance, with 38. 5 RayIoU and 29. 1 mIoU for semantic occupancy, operating at a rapid speed of 43. 9 FPS.
Graphon Particle Systems, Part I: Spatio-Temporal Approximation and Law of Large Numbers
Besides, by constructing two-level approximated functions converging to the graphon mean field terms, we establish the law of large numbers, which reveals that if the number of particles tends to infinity and the discretization step tends to zero, then the discrete-time interacting particle system over a large-scale network converges to the graphon particle system.
Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering
In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering.
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
Joint Uplink and Downlink Rate Splitting for Fog Computing-Enabled Internet of Medical Things
This paper proposes a novel transmit scheme for fog computing-enabled IoMT that leverages uplink and downlink rate splitting (RS).
IFNet: Deep Imaging and Focusing for Handheld SAR with Millimeter-wave Signals
Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings.
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editing": precisely modifying specific features of a subject while maintaining its inherent identity and context.
Latency Correction for Event-guided Deblurring and Frame Interpolation
Event cameras with their high temporal resolution dynamic range and low power consumption are particularly good at time-sensitive applications like deblurring and frame interpolation.
Transfer and Alignment Network for Generalized Category Discovery
On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later.
Passive Non-Line-of-Sight Imaging with Light Transport Modulation
In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network.
Generalized Category Discovery with Large Language Models in the Loop
Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples.
FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin
We apply the FlashOCC to diverse occupancy prediction baselines on the challenging Occ3D-nuScenes benchmarks and conduct extensive experiments to validate the effectiveness.
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Specifically, we retrieve k-nearest neighbors of a query as its positive keys to capture semantic similarities between data and then aggregate information from the neighbors to learn compact cluster representations, which can make fine-grained categories more separatable.
DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud
However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and strong interference and noise.
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer
In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.
DiLogics: Creating Web Automation Programs With Diverse Logics
Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders.
DATE: Domain Adaptive Product Seeker for E-commerce
Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG.
Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI
Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization.
Risk-Aware Objective-Based Forecasting in Inertia Management
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process.
Decentralized Online Learning for Random Inverse Problems Over Graphs
We propose a decentralized online learning algorithm for distributed random inverse problems over network graphs with online measurements, and unifies the distributed parameter estimation in Hilbert spaces and the least mean square problem in reproducing kernel Hilbert spaces (RKHS-LMS).
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness.
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory
Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features.
An Ontology for the Social Determinants of Health Domain
We present a prototype ontology called Social Determinant of Health Ontology (SOHO) that covers relevant concepts and IS--A relationships describing impacts and associations of social determinants.
A monitoring framework for deployed machine learning models with supply chain examples
We use our implementation to study drift in model features, predictions, and performance on three real data sets.
Self-Supervised Intensity-Event Stereo Matching
Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.
Decentralized Policy Gradient for Nash Equilibria Learning of General-sum Stochastic Games
For the case with exact pseudo gradients, we design a two-loop algorithm by the equivalence of Nash equilibrium and variational inequality problems.
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks
In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs).
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data.
Noise-Tolerant Learning for Audio-Visual Action Recognition
To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence.
Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products.
WANDS: Dataset for Product Search Relevance Assessment
We also show that the dataset is effective in evaluating and discriminating between different search models.
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review
This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles.
Integrated routing for a vehicle-robot pickup and delivery system with time constraints
This paper considers an unmanned vehicle-robot pickup and delivery system, in which a self-driving vehicle carrying multiple unmanned robots in the form of the mother ship travels from a depot to a number of stations distributed in a neighborhood to perform multiple pickup and delivery services.
RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals
To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation.
Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface
The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices.
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
Radio-Assisted Human Detection
In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based onestage detectors and two-stage detectors.
Contactless Electrocardiogram Monitoring with Millimeter Wave Radar
Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in.
RFGAN: RF-Based Human Synthesis
To the best of our knowledge, this is the first work to generate optical images based on RF signals.
High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces
Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices.
Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient
A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection.
Unsupervised Domain Adaptation for RF-based Gesture Recognition
Then we propose a confidence constraint loss to enhance the effectiveness of pseudo-labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust.
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations.
Personalized Outfit Recommendation With Learnable Anchors
To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i. e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes.
Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes
As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the fields of autonomous driving, medical imaging, and defense.
Cyclic Arbitrage in Decentralized Exchanges
We find that traders have executed 292, 606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue.
Three-dimensional Kinematic Metamaterial with tuneable directional permeability
Reconfigurable metamaterials are constructed from tessellation of deformable modules that give rise to a set of tuneable properties.
Applied Physics
Distributed Stochastic Optimization With Unbounded Subgradients Over Randomly Time-Varying Networks
Motivated by distributed statistical learning over uncertain communication networks, we study distributed stochastic optimization by networked nodes to cooperatively minimize a sum of convex cost functions.
Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix
In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP).
Is Machine Learning Speaking my Language? A Critical Look at the NLP-Pipeline Across 8 Human Languages
Natural Language Processing (NLP) is increasingly used as a key ingredient in critical decision-making systems such as resume parsers used in sorting a list of job candidates.
Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide
Carbon capture and storage (CCS) can aid decarbonization of the atmosphere to limit further global temperature increases.
Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value
Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three.
Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving
from "The C Programming Language" course and "What are similarities and differences between packet switching and circuit switching?"
Generalizing Monocular 3D Human Pose Estimation in the Wild
We observe that recent innovation in this area mainly focuses on new techniques that explicitly address the generalization issue when using this dataset, because this database is constructed in a highly controlled environment with limited human subjects and background variations.
Ranked #72 on
3D Human Pose Estimation
on Human3.6M
Very Power Efficient Neural Time-of-Flight
Therefore, the design of a power efficient ToF camera always creates a painful dilemma for the illumination and the performance trade-off.
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection
To solve this problem, we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
DroidNative: Semantic-Based Detection of Android Native Code Malware
This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations, provides automation and platform independence, and as far as we know is the first system that operates at the Android native code level, allowing it to detect malware embedded in both native code and bytecode.
Cryptography and Security
Principled Evaluation of Differentially Private Algorithms using DPBench
Differential privacy has become the dominant standard in the research community for strong privacy protection.
Databases Cryptography and Security
Differentially Private Algorithms for Empirical Machine Learning
In this paper, we develop differentially private algorithms that mirror real world empirical machine learning workflows.
Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game.
