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Found 80 papers, 37 papers with code
New Threats against Object Detector with Non-local Block
The experiments show that the universal patches are able to mislead the detector with greater probabilities.
Counterfactual Matters: Intrinsic Probing For Dialogue State Tracking
Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.
Palette of Language Models: A Solver for Controlled Text Generation
Recent advancements in large language models have revolutionized text generation with their remarkable capabilities.
Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs
For specific industrial scenarios, our framework can facilitate the training of specialized agents using domain-specific knowledge graphs for error detection, which highlights the potential industrial application value of our framework.
ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS).
Dual-Schedule Inversion: Training- and Tuning-Free Inversion for Real Image Editing
For real image editing, most diffusion model-based methods use DDIM Inversion as the first stage before editing.
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds
Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as maintaining character fidelity throughout a storyline and navigating open-ended narratives without a definitive ground truth.
DIVE: Taming DINO for Subject-Driven Video Editing
Specifically, to ensure temporal motion consistency, DIVE employs DINO features to align with the motion trajectory of the source video.
Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling
The development of real-time, high-throughput data compression algorithms capable of reducing this data to manageable sizes for permanent storage is of paramount importance.
Variable Rate Neural Compression for Sparse Detector Data
In contrast, emerging deep learning-based models, particularly those utilizing convolutional neural networks for compression, have outperformed these conventional methods in terms of compression ratios and reconstruction accuracy.
Multi-scale Generative Modeling for Fast Sampling
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models have shown that transitioning to the wavelet domain offers a promising alternative.
MagicEraser: Erasing Any Objects via Semantics-Aware Control
However, the object erasure task, which is in increasing demand, aims to erase objects and generate harmonious background.
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA).
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
Our approach integrates brain connectivity data from diffusion tensor imaging (DTI) and functional MRI (fMRI), employing graph neural networks (GNNs) for fused graph classification.
Exploiting Supervised Poison Vulnerability to Strengthen Self-Supervised Defense
Availability poisons exploit supervised learning (SL) algorithms by introducing class-related shortcut features in images such that models trained on poisoned data are useless for real-world datasets.
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
In addition, we propose a well-designed framework comprised of pulse wave probing, pulse wave extraction, and AF detection to ensure accurate and reliable AF detection.
DiffX: Guide Your Layout to Cross-Modal Generative Modeling
However, most diffusion models are limited to visible RGB image generation.
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, whereas their adaptation on image restoration, particularly within computational microscopy remains theoretically and empirically underexplored.
Energy efficiency analysis of ammonia-fueled power systems for vehicles considering residual heat recovery
ICEs and FCs require different amounts of hydrogen, and they produce residual heat of different quality and quantity, so the system efficiency is not only determined by the engine operating point, but also by the measures and ratios of residual heat recovery.
Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.
The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)
Recently, increasing research interests have focused on retrieval augmented generation (RAG) to mitigate hallucination for large language models (LLMs).
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting.
An Alternative Method to Identify the Susceptibility Threshold Level of Device under Test in a Reverberation Chamber
By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the input power and repeating the whole testing many times.
Diffusion Model-Based Image Editing: A Survey
In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
Orion-14B: Open-source Multilingual Large Language Models
In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters.
USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis
In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis.
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis.
Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking
Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys.
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society.
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention.
Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation
To address the lack of labelled datasets for these seal-related tasks, we propose Seal2Real, a generative method that generates a large amount of labelled document seal data, and construct a Seal-DB dataset containing 20K images with labels.
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
BEVHeight++: Toward Robust Visual Centric 3D Object Detection
In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
Graph Edit Distance Learning via Different Attention
To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs.
Robust open-set classification for encrypted traffic fingerprinting
First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets.
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.
Integrating Different Informations for Portfolio Selection
Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior.
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem.
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses.
Learning Higher-order Object Interactions for Keypoint-based Video Understanding
Specifically, KeyNet introduces the use of object based keypoint information to capture context in the scene.
Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets.
UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner.
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains
Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks.
Multi-Action Dialog Policy Learning from Logged User Feedback
To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.
A holistically 3D-printed flexible millimeter-wave Doppler radar: Towards fully printed high-frequency multilayer flexible hybrid electronics systems
Flexible hybrid electronics (FHE) is an emerging technology enabled through the integration of advanced semiconductor devices and 3D printing technology.
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting
Analyzing both temporal and spatial patterns for an accurate forecasting model for financial time series forecasting is a challenge due to the complex nature of temporal-spatial dynamics: time series from different locations often have distinct patterns; and for the same time series, patterns may vary as time goes by.
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems
Second, an important ingredient in a US is that the user goal can be effectively incorporated and tracked; but how to flexibly integrate goal state tracking and develop an end-to-end trainable US for multi-domains has remained to be a challenge.
Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture
Recently, there has been progress in supervised funetuning pretrained GPT-2 to build end-to-end task-oriented dialog (TOD) systems.
Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset
Recently, there have merged a class of task-oriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games.
Generalized Intent Discovery: Learning from Open World Dialogue System
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes.
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models
In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD.
A Challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems
A challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems, Co-located with EMNLP2022 SereTOD Workshop.
Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation
Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks.
V4d: voxel for 4d novel view synthesis
The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task.
Transferable Adversarial Attack based on Integrated Gradients
The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community.
AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation
We show that AggPose outperforms hybrid model HRFormer and TokenPose in the infant pose estimation dataset.
Ranked #7 on
Keypoint Detection
on MS COCO
"Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs
Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses.
Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems
Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems.
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations.
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines.
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Here, we introduce the application of balancing loss functions for multi-label text classification.
Ranked #1 on
Multi-Label Text Classification
on Reuters-21578
Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems
In this paper, we propose Variational Latent-State GPT model (VLS-GPT), which is the first to combine the strengths of the two approaches.
Metal-Insulator Transition in $n$-type bulk crystals and films of strongly compensated SrTiO$_3$
We try to explain this discrepancy in the framework of the theory of the percolation MIT in a strongly compensated semiconductor with the same $N=10^{19}$ cm$^{-3}$.
Materials Science
Learning to Check Contract Inconsistencies
This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning
In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.
A Semi-Blind Multiuser SIMO GFDM System in the Presence of CFOs and IQ Imbalances
A low-complexity semi-blind joint estimation scheme of multiple channels, CFOs and IQ imbalances is proposed.
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems
The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task.
PSF--NET: A Non-parametric Point Spread Function Model for Ground Based Optical Telescopes
Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -- PSF-NET.
Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data
(1) It can model longitudinal heterogeneous EHRs data via capturing the 3-order correlations of different modalities and the irregular temporal impact of historical events.
Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks
Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success.
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison
Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis.
Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features
Texture is one of the most obvious characteristics in solar images and it is normally described by texture features.
Perception Evaluation -- A new solar image quality metric based on the multi-fractal property of texture features
Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques.
Elastic CRFs for Open-ontology Slot Filling
An ontology is defined by the collection of slots and the values that each slot can take.
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e. g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation.
A Tamper-Free Semi-Universal Communication System for Deletion Channels
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model.
Towards a New Science of a Clinical Data Intelligence
We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.
