Search Results for author:
Found 112 papers, 43 papers with code
RepViT: Revisiting Mobile CNN From ViT Perspective
Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency, compared with lightweight Convolutional Neural Networks (CNNs), on resource-constrained mobile devices.
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion.
Ranked #5 on
Multimodal Sentiment Analysis
on CMU-MOSI
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
Ranked #49 on
Link Prediction
on FB15k-237
RepViT-SAM: Towards Real-Time Segmenting Anything
Here, to achieve real-time segmenting anything on mobile devices, following MobileSAM, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model.
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e. g., long-short-term-memory (LSTM).
Ranked #23 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
Generative Knowledge Graph Construction: A Review
Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner.
Ranked #18 on
Cross-Modal Retrieval
on Flickr30k
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection Approach
The concept of \emph{wrapped phased-difference pattern} (WPDP) is introduced, which allows the proposed algorithm to compute most of the parameters required for DOA estimation in an offline manner, hence resulting in a superior computational speed in realtime.
A Tutorial on Terahertz-Band Localization for 6G Communication Systems
Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond.
Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation
We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set.
Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective
Radio localization is applied in high-frequency (e. g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure.
OntoED: Low-resource Event Detection with Ontology Embedding
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
Geometry-Contrastive GAN for Facial Expression Transfer
Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.
CodeInstruct: Empowering Language Models to Edit Code
In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring.
InstructCoder: Instruction Tuning Large Language Models for Code Editing
In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models
Experiments reveal that models incorporating the proposed MiLe Loss can gain consistent performance improvement on downstream benchmarks.
Ranked #89 on
Multi-task Language Understanding
on MMLU
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation.
A Variational Approach for Learning from Positive and Unlabeled Data
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Knowledge Perceived Multi-modal Pretraining in E-commerce
In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification.
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences
Existing multimodal tasks mostly target at the complete input modality setting, i. e., each modality is either complete or completely missing in both training and test sets.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
Text-guided Legal Knowledge Graph Reasoning
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
Ruleformer: Context-aware Rule Mining over Knowledge Graph
Based on this idea, we propose a transformer-based rule mining approach, Ruleformer.
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning.
Multi-Agent Trust Region Learning
We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
To address this issue, recent works consider the Few-shot Unsupervised Domain Adaptation (FUDA) where only a few source samples are labeled, and conduct knowledge transfer via self-supervised learning methods.
Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters
In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients.
Deep Structural Estimation: With an Application to Option Pricing
We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks.
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs).
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference.
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
Video question-answering is a fundamental task in the field of video understanding.
INSTRAUG: Automatic Instruction Augmentation for Multimodal Instruction Fine-tuning
Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on new tasks.
Detecting Network-based Internet Censorship via Latent Feature Representation Learning
To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
3D Model-Based Continuous Emotion Recognition
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications.
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation
In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation.
BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS
This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set.
A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms.
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
A new Majorana platform in an Fe-As bilayer superconductor
On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors.
Superconductivity Strongly Correlated Electrons
Learning to Infer Unseen Attribute-Object Compositions
The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people.
Persuasive Dialogue Understanding: the Baselines and Negative Results
Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.
Order of magnitude increase in laser-target coupling at near-relativistic intensities using compound parabolic concentrators
Achieving a high conversion efficiency into relativistic electrons is central to short-pulse laser application and fundamentally relies on creating interaction regions with intensities ${\gg}10^{18}$~W/cm$^2$.
Plasma Physics
Physical rendering of synthetic spaces for topological sound transport
Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.
Mesoscale and Nanoscale Physics Classical Physics
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Generative-Adversarial-Networks-based Ghost Recognition
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
A Derivation of Identifiable Condition for Non-Uniform Linear Array DOA Estimation
Phase ambiguity happens in uniform linear arrays (ULAs) when the sensor distance is greater than $\lambda/2$.
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.
0.8% Nyquist computational ghost imaging via non-experimental deep learning
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns.
Ranked #1 on
Open-Domain Question Answering
on Natural Questions
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.
Multi-trends Enhanced Dynamic Micro-video Recommendation
In this paper, we propose the DMR framework to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends.
Ghost Imaging Based on Recurrent Neural Network
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination
Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS).
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks
Quantization is applied to reduce the latency and storage cost of CNNs.
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data
This motivates the use of commodity sensors like cameras for data collection, which are an order of magnitude cheaper than HD sensor suites, but offer lower fidelity.
MCRB-based Performance Analysis of 6G Localization under Hardware Impairments
Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing
6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing.
Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization
With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array.
Bi-Static Sensing for Near-Field RIS Localization
Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work.
Doppler Exploitation in Bistatic mmWave Radio SLAM
Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM).
Neural-Symbolic Entangled Framework for Complex Query Answering
In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.
Localization Coverage Analysis of THz Communication Systems with a 3D Array
This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations.
Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface
The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing.
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities
Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing.
Misspecified Cramér-Rao Bound of RIS-aided Localization under Geometry Mismatch
In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios.
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition
Modeling the speaker variability is a key challenge for automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Joint RIS Calibration and Multi-User Positioning
Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform.
RIS-Aided Monostatic Sensing and Object Detection with Single and Double Bounce Multipath
We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation.
RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation.
Modeling and Analysis of OFDM-based 5G/6G Localization under Hardware Impairments
Our extensive simulation results demonstrate that each type of HWI leads to a different level of degradation in angle and delay estimation performance, and the prominent impairment factors on delay estimation will have a dominant negative effect on SER.
RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing
A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people).
MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring
However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information.
A Surrogate-Assisted Highly Cooperative Coevolutionary Algorithm for Hyperparameter Optimization in Deep Convolutional Neural Network
Convolutional neural networks (CNNs) have gained remarkable success in recent years.
Multi-RIS-Enabled 3D Sidelink Positioning
Positioning is expected to be a core function in intelligent transportation systems (ITSs) to support communication and location-based services, such as autonomous driving, traffic control, etc.
Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport
Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications.
JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems
In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters.
Bayesian Federated Learning: A Survey
This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.
Zero Access Points 3D Cooperative Positioning via RIS and Sidelink Communications
Reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks, due to their capability to create a controllable wireless environment, and achieve extended coverage and improved localization accuracy.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface
The system requires the shortest time and has the best performance for processing color images.
6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness
6G promises a paradigm shift in which positioning and sensing are inherently integrated, enhancing not only the communication performance but also enabling location- and context-aware services.
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs
We further employ a consistent dynamic channel pruning (CDCP) strategy to dynamically prune unimportant channels in ViTs.
Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data
This study aimed to develop an efficient multi-modal fusion architecture for medical data that was robust to missing modalities and further improved the performance on disease diagnosis. X-ray chest radiographs for the image modality, radiology reports for the text modality, and structured value data for the tabular data modality were fused in this study.
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations
BPFL aims to quantify the uncertainty and heterogeneity within and across clients towards uncertainty representations by addressing the statistical heterogeneity of client data.
RIS Position and Orientation Estimation via Multi-Carrier Transmissions and Multiple Receivers
In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i. e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations.
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser
To address the above limitations, we propose DiffusionTalker, a diffusion-based method that utilizes contrastive learning to personalize 3D facial animation and knowledge distillation to accelerate 3D animation generation.
Re-parameterized Low-rank Prompt: Generalize a Vision-Language Model within 0.5K Parameters
On the other hand, complicated structures and update rules largely increase the computation and storage cost.
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
On Smart Morphing Wing Aircraft Robust Adaptive Beamforming
To overcome this problem and ensure robust beamforming for FCA, deviations in array control parameters (ACPs) and array perturbations, the effect of mutual coupling in addition to looking-direction errors should be considered.
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD).
ELAA Near-Field Localization and Sensing with Partial Blockage Detection
Considering the array size could be much larger than an obstacle, the effect of partial blockage (i. e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed.
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Data users have been endowed with the right to be forgotten of their data.
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation
Consequently, a simple combination of them cannot guarantee accomplishing both training efficiency and inference efficiency with minimal costs.
