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Found 48 papers, 20 papers with code
Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology
By integrating the above three techniques, the whole method is capable of identifying various types of outliers in a reliable and adaptive manner.
DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy.
Ranked #11 on
Bench2Drive
on Bench2Drive
Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger
Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging external tools for complex problems that require specialized processing or real-time data.
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models
Guided diffusion models, leveraging the potent generative priors of pre-trained models along with a differential guidance loss, have achieved promising results in blind image restoration.
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation
Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while maintaining safety and comfort standards.
Rank Aggregation in Crowdsourcing for Listwise Annotations
To our knowledge, LAC is the first work to directly deal with the full rank aggregation problem in listwise crowdsourcing, and simultaneously infer the difficulty of problems, the ability of annotators, and the ground-truth ranks in an unsupervised way.
Automatic detection of Mild Cognitive Impairment using high-dimensional acoustic features in spontaneous speech
Experiment 3 further enhanced the language-agnostic model from Experiment 1, with a specific focus on evaluating the robustness of the models using out-of-sample test data.
A Hybrid Framework for Spatial Interpolation: Merging Data-driven with Domain Knowledge
Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies.
Prosody of speech production in latent post-stroke aphasia
We also investigated the diagnostic classification of latent aphasia versus neurotypical control using random forest, aiming to build a fast and reliable tool to assist with the identification of latent aphasia.
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems.
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution
As virtual and augmented reality applications gain popularity, omnidirectional image (ODI) super-resolution has become increasingly important.
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) with retrieved external knowledge.
Planning with Multi-Constraints via Collaborative Language Agents
This paper introduces Planning with Multi-Constraints (PMC), a zero-shot methodology for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks.
SCALM: Towards Semantic Caching for Automated Chat Services with Large Language Models
In this work, we for the first time conducted an analysis on real-world human-to-LLM interaction data, identifying key challenges in existing caching solutions for LLM-based chat services.
Multi-view Content-aware Indexing for Long Document Retrieval
As they do not consider content structures, the resultant chunks can exclude vital information or include irrelevant content.
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs).
Aligning Crowd Feedback via Distributional Preference Reward Modeling
We propose the Distributional Preference Reward Model (DPRM), a simple yet effective framework to align large language models with diverse human preferences.
Towards Progressive Multi-Frequency Representation for Image Warping
Image warping a classic task in computer vision aims to use geometric transformations to change the appearance of images.
Evaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Study
A real-world roundabout in Ann Arbor, Michigan was built in the co-simulation platform as the study area, and the merging scenarios were investigated.
Semi-supervised Medical Image Segmentation via Query Distribution Consistency
In this paper, we propose a novel Dual KMax UX-Net framework that leverages labeled data to guide the extraction of information from unlabeled data.
Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark
In this paper, we provide a comprehensive study on a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images.
PGDQN: Preference-Guided Deep Q-Network
Stochastic exploration is the key to the success of the Deep Q-network (DQN) algorithm.
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection
One straightforward way to address this issue is to simultaneous process multi-face by integrating face extraction and forgery detection in an end-to-end fashion by adapting advanced object detection architectures.
HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace
Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages.
Decision-making with Speculative Opponent Models
To address this issue, we introduce Distributional Opponent-aided Multi-agent Actor-Critic (DOMAC), the first speculative opponent modelling algorithm that relies solely on local information (i. e., the controlled agent's observations, actions, and rewards).
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics.
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding.
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections.
Deep Progressive Feature Aggregation Network for High Dynamic Range Imaging
The primary challenges are ghosting artifacts caused by object motion between low dynamic range images and distorted content in under and overexposed regions.
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.
Applying Feature Underspecified Lexicon Phonological Features in Multilingual Text-to-Speech
This mapping was tested for whether it could lead to the successful generation of native, non-native, and code-switched speech in the two languages.
ByT5 model for massively multilingual grapheme-to-phoneme conversion
In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback
Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance.
Phone-to-audio alignment without text: A Semi-supervised Approach
The task of phone-to-audio alignment has many applications in speech research.
Applying Phonological Features in Multilingual Text-To-Speech
We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech in the two languages.
Synchronising speech segments with musical beats in Mandarin and English singing
Generating synthesised singing voice with models trained on speech data has many advantages due to the models' flexibility and controllability.
DeepFake-o-meter: An Open Platform for DeepFake Detection
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
Comparing acoustic analyses of speech data collected remotely
Face-to-face speech data collection has been next to impossible globally due to COVID-19 restrictions.
Relating spin-foam to canonical loop quantum gravity by graphical calculus
On the other hand, the action of a Euclidean Hamiltonian constraint operator on certain spin network states is calculated by graphical method.
General Relativity and Quantum Cosmology
VSEGAN: Visual Speech Enhancement Generative Adversarial Network
This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN).
Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images
With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis.
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets
The improved deep reinforcement learning network is then used to search for and learn the hyperparameters of each sample point in the inverse distance weighted model.
Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count.
Ranked #15 on
Crowd Counting
on WorldExpo’10
