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Found 112 papers, 47 papers with code
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing
Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence.
Generic Mechanism for Reducing Repetitions in Encoder-Decoder Models
Encoder-decoder models have been commonly used for many tasks such as machine translation and response generation.
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information
Recently, zero-shot entity linking task has become a research hotspot, which links mentions to unseen entities to challenge the generalization ability.
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
As a result, we obtain the best known upper bound estimates in terms of key quantities of interest, such as the dimension and rates of convergence, for the Wasserstein-2 distance between the data distribution (Gaussian with unknown mean) and our sampling algorithm.
Modelling the Formation of Peer-to-Peer Trading Coalitions and Prosumer Participation Incentives in Transactive Energy Communities
Our experimental study shows that, for both market models, only a small number of P2P contracts, and only a fraction of total prosumers in the community are required to achieve the majority of the maximal potential Gains from Trade.
Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data
Distribution system state estimation (DSSE) is paramount for effective state monitoring and control.
Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control
Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies.
A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud
In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human-computer interaction computing mode that can be implemented in cloud.
High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models
To address these issues, we propose a minimally-supervised high-fidelity speech synthesis method, where all modules are constructed based on the diffusion models.
pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning
Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides. The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods.
LLMCad: Fast and Scalable On-device Large Language Model Inference
Currently, the execution of these generative tasks heavily depends on Large Language Models (LLMs).
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Fine-tuning pre-trained models has emerged as a powerful technique in numerous domains, owing to its ability to leverage enormous pre-existing knowledge and achieve remarkable performance on downstream tasks.
A Robust ADMM-Based Optimization Algorithm For Underwater Acoustic Channel Estimation
Accurate estimation of the Underwater acoustic (UWA) is a key part of underwater communications, especially for coherent systems.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
Dark-Skin Individuals Are at More Risk on the Street: Unmasking Fairness Issues of Autonomous Driving Systems
This paper conducts fairness testing on automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems.
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching
In short, given a cold-start song request, we expect to retrieve songs with similar audiences and then fastly push the cold-start song to the audiences of the retrieved songs to warm up it.
How to Build Low-cost Networks for Large Language Models (without Sacrificing Performance)?
We show that LLMs exhibit a unique communication pattern where only small groups of GPUs require high-bandwidth communication to achieve near-optimal training performance.
Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles
Although news articles contain travel information of celebrities, it is not possible to perform large-scale and network-wise analysis due to the lack of automatic itinerary detection tools.
Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs
Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations.
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis
Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments.
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and (3) the resilience of ChatGPT against these jailbreak prompts.
A Study of Generative Large Language Model for Medical Research and Healthcare
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
Bidirectional Transformer Reranker for Grammatical Error Correction
Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task.
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment
Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists.
A Mountain-Shaped Single-Stage Network for Accurate Image Restoration
Image restoration is the task of aiming to obtain a high-quality image from a corrupt input image, such as deblurring and deraining.
Ranked #1 on
Image Deblurring
on HIDE
Plug-and-Play Regulators for Image-Text Matching
Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching.
Ranked #2 on
Image Retrieval
on Flickr30K 1K test
Limits of Predictability in Top-N Recommendation
This work investigates such predictability by studying the degree of regularity from a specific set of user behavior data.
Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis
Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre.
Improving the quality of dental crown using a Transformer-based method
To this end, we use a geometry-aware transformer to generate dental crowns.
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents.
Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks
A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived.
Audio2Gestures: Generating Diverse Gestures from Audio
Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline.
BadPrompt: Backdoor Attacks on Continuous Prompts
To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts.
Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design
For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix.
Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
We introduce a new Langevin dynamics based algorithm, called e-TH$\varepsilon$O POULA, to solve optimization problems with discontinuous stochastic gradients which naturally appear in real-world applications such as quantile estimation, vector quantization, CVaR minimization, and regularized optimization problems involving ReLU neural networks.
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion
Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances
In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances.
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling
To capture the semantic dependency between the syntactic information and task labels, we combine the task specific features with corresponding label embeddings by attention mechanism.
Towards Higher-order Topological Consistency for Unsupervised Network Alignment
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications.
Semi-supervised segmentation of tooth from 3D Scanned Dental Arches
In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets.
Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review
The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.
Multi-scale Attentive Image De-raining Networks via Neural Architecture Search
The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task.
Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving
In this work, we propose a systematic study on simple Copy-Paste data augmentation for rare object detection in autonomous driving.
Probabilistic Models for Manufacturing Lead Times
In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes.
Mix-up Self-Supervised Learning for Contrast-agnostic Applications
Contrastive self-supervised learning has attracted significant research attention recently.
Influence of the vessel wall geometry on the wall-induced migration of red blood cells
The geometry of the blood vessel wall plays a regulatory role on the motion of red blood cells (RBCs).
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
Detailed Balance for Particle Models of Reversible Reactions in Bounded Domains
In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction-fluxes at all points requires spatially varying unbinding rate functions near the domain boundary.
Distribution Consistent Neural Architecture Search
We first theoretically investigate how the weight coupling problem affects the network searching performance from a parameter distribution perspective, and then propose a novel supernet training strategy with a Distribution Consistent Constraint that can provide a good measurement for the extent to which two architectures can share weights.
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor
Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention.
Building an AI-ready RSE Workforce
Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception.
Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.
SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation
The repeated cross-correlation and semi-FPN are designed based on this idea.
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Sensors are the key to sensing the environment and imparting benefits to smart cities in many aspects, such as providing real-time air quality information throughout an urban area.
Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.
Ranked #3 on
Gesture Generation
on BEAT
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization
Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems.
Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition
In this paper, we tackle the problem by transferring knowledge from three aspects, i. e., domain, language and task, and strengthening connections among them.
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
To illustrate the applicability of the main results, we consider an example from transfer learning with ReLU neural networks, which represents a key paradigm in machine learning.
Animatable Neural Radiance Fields from Monocular RGB Videos
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.
Model-based 3D Hand Reconstruction via Self-Supervised Learning
For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
Quantifying measurement-induced nonbilocal correlation
In the paper, we devote to defining an available measure to quantify the nonbilocal correlation in the entanglement-swapping experiment.
Quantum Physics
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning
Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies.
Similarity Reasoning and Filtration for Image-Text Matching
Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words.
Ranked #3 on
Image Retrieval
on Flickr30K 1K test
A Unified Spectral Sparsification Framework for Directed Graphs
For the first time, we prove the existence of linear-sized spectral sparsifiers for general directed graphs and introduce a practically-efficient and unified spectral graph sparsification approach that allows sparsifying real-world, large-scale directed and undirected graphs with guaranteed preservation of the original graph spectra.
Existence and uniqueness of local weak solution of d-dimensional tropical climate model without thermal diffusion in inhomogeneous Besov space
This paper studies the existence and uniqueness of local weak solutions to the d-dimensional tropical climate model without thermal diffusion.
Analysis of PDEs
Emotional Conversation Generation with Heterogeneous Graph Neural Network
Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i. e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system.
SF-GRASS: Solver-Free Graph Spectral Sparsification
Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc.
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating
We are thus able to provide theoretical guarantees for the algorithm's convergence in (standard) Wasserstein distances for both convex and non-convex objective functions.
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.
Binarized Graph Neural Network
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
Machine learning on DNA-encoded libraries: A new paradigm for hit-finding
We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds.
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
In this paper, we are concerned with a non-asymptotic analysis of sampling algorithms used in nonconvex optimization.
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
Retrieving Signals with Deep Complex Extractors
Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.
Improving Captioning for Low-Resource Languages by Cycle Consistency
Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.
E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation
The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data.
A Survey and Experimental Analysis of Distributed Subgraph Matching
We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Databases
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017).
RecovDB: accurate and efficient missing blocks recovery for large time series
In this paper, we also compare the efficiency and accuracy of RECOVDB against state-of-the-art recovery systems.
Voiceprint recognition of Parkinson patients based on deep learning
In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features.
Multivariate Time Series Imputation with Generative Adversarial Networks
Multivariate time series usually contain a large number of missing values, which hinders the application of advanced analysis methods on multivariate time series data.
Deep Cross-Modal Projection Learning for Image-Text Matching
The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs.
Ranked #18 on
Text based Person Retrieval
on CUHK-PEDES
Fashion-Gen: The Generative Fashion Dataset and Challenge
We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models.
Ranked #19 on
Speech Recognition
on TIMIT
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Medical Concept Embedding with Time-Aware Attention
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
IDEL: In-Database Entity Linking with Neural Embeddings
We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities.
Online Product Quantization
The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
A core business in the fashion industry is the understanding and prediction of customer needs and trends.
Device-to-Device Load Balancing for Cellular Networks
The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency.
Networking and Internet Architecture
Deep Mutual Learning
Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network.
Deep Complex Networks
Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.
Ranked #3 on
Music Transcription
on MusicNet
Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems
This paper considers a general data-fitting problem over a networked system, in which many computing nodes are connected by an undirected graph.
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
End-to-End Data Visualization by Metric Learning and Coordinate Transformation
This paper presents a deep nonlinear metric learning framework for data visualization on an image dataset.
Recurrent Neural Networks With Limited Numerical Precision
Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often high.
Professor Forcing: A New Algorithm for Training Recurrent Networks
We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps.
Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.
Databases
Recurrent Neural Networks With Limited Numerical Precision
We present results from the use of different stochastic and deterministic reduced precision training methods applied to three major RNN types which are then tested on several datasets.
On Multiplicative Integration with Recurrent Neural Networks
We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs).
Sample-Specific SVM Learning for Person Re-Identification
Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Batch Normalized Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies.
Subspace Clustering by Mixture of Gaussian Regression
Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG).
