Search Results for author:
Found 297 papers, 100 papers with code
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP).
BMInf: An Efficient Toolkit for Big Model Inference and Tuning
In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Wireless networks are increasingly facing challenges due to their expanding scale and complexity.
Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications
The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios.
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model
Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos.
Exploring Selective Layer Fine-Tuning in Federated Learning
Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner.
ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
Only a few research explored the conflicts between the inherent knowledge of LLMs and the retrieved contextual knowledge.
A Landscape-Aware Differential Evolution for Multimodal Optimization Problems
First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak.
Task-Oriented Communication for Vehicle-to-Infrastructure Cooperative Perception
Despite its potential to improve perception accuracy and robustness, the large amount of raw sensor data inevitably results in high communication overhead.
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software.
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
With the rapid advancement of stereo vision technologies, stereo image compression has emerged as a crucial field that continues to draw significant attention.
Multimodal contrastive learning for spatial gene expression prediction using histology images
In this paper, we propose \textbf{mclSTExp}, a multimodal contrastive learning with Transformer and Densenet-121 encoder for Spatial Transcriptomics Expression prediction.
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Let the Code LLM Edit Itself When You Edit the Code
Simply encoding the edited subsequence and integrating it to the original KV cache meets the temporal confusion problem, leading to significantly worse performance.
Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models
Moreover, RCDMs can generate consistent stories with a single forward inference compared to autoregressive models.
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
However, further improvement of RL algorithms in the IC domain is impeded due to two limitations of online experience.
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR
Finally, we explore training and inference methods to mitigate high insertion errors.
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for downlink channel state information (CSI).
CityBench: Evaluating the Capabilities of Large Language Model as World Model
Based on CitySim, we design 7 tasks in 2 categories of perception-understanding and decision-making group to evaluate the capability of LLMs as city-scale world model for urban domain.
Timo: Towards Better Temporal Reasoning for Language Models
Therefore, we propose a crucial question: Can we build a universal framework to handle a variety of temporal reasoning tasks?
SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words
We also conduct a comprehensive evaluation using objective evaluation methods (e. g. BLEU and ROUGE), subjective evaluations and LLM-based metrics for the generated responses.
A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking
Based on the simulator, we implement a set of microscopic and macroscopic controllable objects and metrics to support most typical transportation system optimization scenarios.
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world.
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation
In this paper, we study the cooperative resource allocation problem with unknown system dynamics of MRPs.
Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR
This paper addresses challenges in integrating new languages into a pre-trained multilingual automatic speech recognition (mASR) system, particularly in scenarios where training data for existing languages is limited or unavailable.
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection.
Memorization in deep learning: A survey
Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the intricacies of DNN decision-making and learning processes remains a significant challenge.
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
In our work on portrait video generation, we identified audio signals as particularly weak, often overshadowed by stronger signals such as facial pose and reference image.
DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention
The increasing difficulty in accurately detecting forged images generated by AIGC(Artificial Intelligence Generative Content) poses many risks, necessitating the development of effective methods to identify and further locate forged areas.
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
A graph neural network (GNN) is then leveraged to exploit the relationships between passages and improve the retrieval of supporting passages.
Freeplane: Unlocking Free Lunch in Triplane-Based Sparse-View Reconstruction Models
We first analyze the role of triplanes in feed-forward methods and find that the inconsistent multi-view images introduce high-frequency artifacts on triplanes, leading to low-quality 3D meshes.
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments.
Ensembling Diffusion Models via Adaptive Feature Aggregation
In this paper, we propose a novel ensembling method, Adaptive Feature Aggregation (AFA), which dynamically adjusts the contributions of multiple models at the feature level according to various states (i. e., prompts, initial noises, denoising steps, and spatial locations), thereby keeping the advantages of multiple diffusion models, while suppressing their disadvantages.
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
We consider the distributed learning problem with data dispersed across multiple workers under the orchestration of a central server.
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
In this paper, we present Vista, a generalizable driving world model with high fidelity and versatile controllability.
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights.
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature.
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration
However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family.
Neural Graph Mapping for Dense SLAM with Efficient Loop Closure
Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation.
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds
Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem.
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training.
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios.
GenAD: Generalized Predictive Model for Autonomous Driving
In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline.
Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations.
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage.
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach
Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures.
Training-Free Long-Context Scaling of Large Language Models
The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length.
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients.
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system.
FD-Vision Mamba for Endoscopic Exposure Correction
In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making.
Private Knowledge Sharing in Distributed Learning: A Survey
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
Spatial-Aware Latent Initialization for Controllable Image Generation
Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems.
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution.
TransLoc4D: Transformer-based 4D Radar Place Recognition
To validate TransLoc4D we construct two datasets and set up benchmarks for 4D radar place recognition.
Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms using Molecular Configuration Transformer
We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.
Context-aware Communication for Multi-agent Reinforcement Learning
Following this, agents utilize attention mechanisms in the second stage to selectively generate messages personalized for the receivers.
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm.
Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
Linear Attention via Orthogonal Memory
Given that orthogonal memory compresses global information, we further dissect the context to amplify fine-grained local information.
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments.
Green Edge AI: A Contemporary Survey
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
Sluggish and Chemically-Biased Interstitial Diffusion in Concentrated Solid Solution Alloys: Mechanisms and Methods
Interstitial diffusion is a pivotal process that governs the phase stability and irradiation response of materials in non-equilibrium conditions.
Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks.
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer
Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel.
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents.
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space.
Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition
On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery.
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Due to the absence of autoregressive modeling and optical flow alignment, we can design an extremely minimalist framework that can greatly benefit computational efficiency.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity.
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance.
Mode Connectivity and Data Heterogeneity of Federated Learning
Empirically, reducing data heterogeneity makes the connectivity on different paths more similar, forming more low-error overlaps between client and global modes.
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics.
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages.
Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques
Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks.
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
This approach is characterized by a two-stage process: drafting and verification.
Client-side Gradient Inversion Against Federated Learning from Poisoning
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
5) Considered both middle- and large- scale outdoor environments, i. e., the 6 trajectories range from 246m to 6. 95km.
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive Learning
Forensic pathology is critical in analyzing death manner and time from the microscopic aspect to assist in the establishment of reliable factual bases for criminal investigation.
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation.
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model.
Ranked #2 on
Personalized Image Generation
on DreamBooth
A new solution and concrete implementation steps for Artificial General Intelligence
At present, the mainstream artificial intelligence generally adopts the technical path of "attention mechanism + deep learning" + "reinforcement learning".
Feature Matching Data Synthesis for Non-IID Federated Learning
For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features.
Binary Federated Learning with Client-Level Differential Privacy
To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations with more extensive histories.
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server.
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer
Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition.
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
We also observe the cross-modal representation extracted by CIF-PT obtains better performance than other neural interfaces for the tasks of SLU, including the dominant speech representation learned from self-supervised pre-training.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection
These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude.
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access
Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
To gain further insights into model averaging in FL, we decompose the expected loss of the global model into five factors related to the client models.
Novel deep learning methods for 3D flow field segmentation and classification
Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow.
Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales.
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server.
Security and Privacy Problems in Voice Assistant Applications: A Survey
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Evaluation of a Canonical Image Representation for Sidescan Sonar
In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion.
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization
Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization.
Grant-free Massive Random Access with Retransmission: Receiver Optimization and Performance Analysis
Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm.
A Survey on Distributed Evolutionary Computation
Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment.
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
While federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt to heterogeneous clients.
Generalized Relation Modeling for Transformer Tracking
Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain.
When Evolutionary Computation Meets Privacy
To this end, in this paper, we discuss three typical optimization paradigms (i. e., \textit{centralized optimization, distributed optimization, and data-driven optimization}) to characterize optimization modes of evolutionary computation and propose BOOM to sort out privacy concerns in evolutionary computation.
Low-complexity Deep Video Compression with A Distributed Coding Architecture
This has inspired a distributed coding architecture aiming at reducing the encoding complexity.
DSDP: A Blind Docking Strategy Accelerated by GPUs
In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning
Learning communication strategies in cooperative multi-agent reinforcement learning (MARL) has recently attracted intensive attention.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance
The features of RGB reference images are then processed by a multi-stage alignment module to explicitly align the features of RGB reference with the LR HSI.
LDMIC: Learning-based Distributed Multi-view Image Coding
Multi-view image compression plays a critical role in 3D-related applications.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing.
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification.
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models
Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences.
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect.
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models.
A Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types
One of the key settings in SANE is the search space defined by cells and organs self-adapted to different DNN types.
Task-Oriented Communication for Edge Video Analytics
With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge.
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
This enables client models to be updated in a shared feature space with consistent classifiers during local training.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance.
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices.
Deep Latent Mixture Model for Recommendation
Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications.
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
In this paper, we propose Comprehensive Attention Benchmark (CAB) under a fine-grained attention taxonomy with four distinguishable attention patterns, namely, noncausal self, causal self, noncausal cross, and causal cross attentions.
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
Multi-dataset Training of Transformers for Robust Action Recognition
We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition.
